50+ ETL Jobs in India

Work Schedule: 4 days work from office with rotational shifts, including night shifts as per business requirements.

Required Skills:

• Bachelor’s degree in Computer Science or similar field or equivalent work experience
• 3+ years of experience on Data Warehousing, Data Engineering or Data Integration projects
• Expert with data warehousing concepts, strategies, and tools
• Strong SQL background
• Strong knowledge of relational databases like SQL Server, PostgreSQL, MySQL
• Strong experience in GCP & Google BigQuery, Cloud SQL, Composer (Airflow), Dataflow, Dataproc, Cloud Function and GCS
• Good to have knowledge on SQL Server Reporting Services (SSRS), and SQL Server Integration Services (SSIS)
• Good to have a Mainframe skillset
• Experience in Informatica Power exchange for Mainframe, Salesforce, and other new-age data sources
• Experience in integration using APIs, XML, JSONs etc.
• In-depth understanding of database management systems, online analytical processing (OLAP) and ETL (Extract, transform, load) framework, data-warehousing and Data Lakes
• Good understanding of SDLC, Agile and Scrum processes
• Strong problem-solving, multi-tasking, and organizational skills
• Highly proficient in working with large volumes of business data and strong understanding of database design and implementation
• Good written and verbal communication skills
• Demonstrated experience of leading a team spread across multiple locations

Roles & Responsibilities:

• Work with business users and other stakeholders to understand business processes
• Ability to design and implement Dimensional and Fact tables
• Identify and implement data transformation/cleansing requirements
• Develop a highly scalable, reliable, and high-performance data processing pipeline to extract, transform and load data from various systems to the Enterprise Data Warehouse
• Develop conceptual, logical, and physical data models with associated metadata including data lineage and technical data definitions
• Design, develop and maintain ETL workflows and mappings using the appropriate data load technique
• Provide research, high-level design, and estimates for data transformation and data integration from source applications to end-user BI solutions
• Provide production support of ETL processes to ensure timely completion and availability of data in the data warehouse for reporting use
• Analyze and resolve problems and provide technical assistance as necessary
• Partner with the BI team to evaluate, design, develop BI reports and dashboards according to functional specifications while maintaining data integrity and data quality
• Work collaboratively with key stakeholders to translate business information needs into well-defined data requirements to implement the BI solutions
• Leverage transactional information, data from ERP, CRM, HRIS applications to model, extract and transform into reporting & analytics
• Define and document the use of BI through user experience/use cases, prototypes, test, and deploy BI solutions
• Develop and support data governance processes, analyze data to identify and articulate trends, patterns, outliers, quality issues, and continuously validate reports, dashboards and suggest improvements
• Train business end-users, IT analysts, and developers

Data Engineer — AI / BI

Artificial Intelligence & Business Intelligence | Data & Analytics

Who We Are:

Since our inception back in 2006, Navitas has grown to be an industry leader in the digital transformation space, and we’ve served as trusted advisors supporting our client base within the commercial, federal, and state and local markets.

What We Do:

At our very core, we’re a group of problem solvers providing our award-winning technology solutions to drive digital acceleration for our customers! With proven solutions, award-winning technologies, and a team of expert problem solvers, Navitas has consistently empowered customers to use technology as a competitive advantage and deliver cutting-edge transformative solutions.

Position Overview

We are seeking a Databricks Engineer to design, build, and operate a Data & AI platform with a strong foundation in the Medallion Architecture (raw/bronze, curated/silver, and mart/gold layers). This platform will orchestrate complex data workflows and scalable ELT pipelines to integrate data from enterprise systems such as PeopleSoft, D2L, and Salesforce, delivering high-quality, governed data for machine learning, AI/BI, and analytics at scale.

You will play a critical role in engineering the infrastructure and workflows that enable seamless data flow across the enterprise, ensure operational excellence, and provide the backbone for strategic decision-making, predictive modeling, and innovation

Responsibilities:

Data & AI Platform Engineering (Databricks-Centric):

• Design, implement, and optimize end-to-end data pipelines on Databricks, following the Medallion Architecture principles.
• Build robust and scalable ETL/ELT pipelines using Apache Spark and Delta Lake to transform raw (bronze) data into trusted curated (silver) and analytics-ready (gold) data layers.
• Operationalize Databricks Workflows for orchestration, dependency management, and pipeline automation.
• Apply schema evolution and data versioning to support agile data development.

Platform Integration & Data Ingestion:

• Connect and ingest data from enterprise systems such as PeopleSoft, D2L, and Salesforce using APIs, JDBC, or other integration frameworks.
• Implement connectors and ingestion frameworks that accommodate structured, semi-structured, and unstructured data.
• Design standardized data ingestion processes with automated error handling, retries, and alerting.

 Data Quality, Monitoring, and Governance:

• Develop data quality checks, validation rules, and anomaly detection mechanisms to ensure data integrity across all layers.
• Integrate monitoring and observability tools (e.g., Databricks metrics, Grafana) to track ETL performance, latency, and failures.
• Implement Unity Catalog or equivalent tools for centralized metadata management, data lineage, and governance policy enforcement.

Security, Privacy, and Compliance:

• Enforce data security best practices including row-level security, encryption at rest/in transit, and fine-grained access control via Unity Catalog.
• Design and implement data masking, tokenization, and anonymization for compliance with privacy regulations (e.g., GDPR, FERPA).
• Work with security teams to audit and certify compliance controls.

   AI/ML-Ready Data Foundation:

• Enable data scientists by delivering high-quality, feature-rich data sets for model training and inference.
• Support AIOps/MLOps lifecycle workflows using MLflow for experiment tracking, model registry, and deployment within Databricks.
• Collaborate with AI/ML teams to create reusable feature stores and training pipelines.

Cloud Data Architecture and Storage:

• Architect and manage data lakes on Azure Data Lake Storage (ADLS) or Amazon S3, and design ingestion pipelines to feed the bronze layer.
• Build data marts and warehousing solutions using platforms like Databricks.
• Optimize data storage and access patterns for performance and cost-efficiency.

 Documentation & Enablement:

• Maintain technical documentation, architecture diagrams, data dictionaries, and runbooks for all pipelines and components.
• Provide training and enablement sessions to internal stakeholders on the Databricks platform, Medallion Architecture, and data governance practices.
• Conduct code reviews and promote reusable patterns and frameworks across teams.

  Reporting and Accountability:

• Submit a weekly schedule of hours worked and progress reports outlining completed tasks, upcoming plans, and blockers.
• Track deliverables against roadmap milestones and communicate risks or dependencies.

Required Qualifications:

• Hands-on experience with Databricks, Delta Lake, and Apache Spark for large-scale data engineering.
• Deep understanding of ELT pipeline development, orchestration, and monitoring in cloud-native environments.
• Experience implementing Medallion Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in enterprise grade environments.
• Strong proficiency in SQL, Python, or Scala for data transformations and workflow logic.
• Proven experience integrating enterprise platforms (e.g., PeopleSoft, Salesforce, D2L) into centralized data platforms.
• Familiarity with data governance, lineage tracking, and metadata management tools.

Preferred Qualifications:

• Prior UMGC or USM experience preferred.
• Experience with Databricks Unity Catalog for metadata management and access control.
• Experience deploying ML models at scale using MLFlow or similar MLOps tools.
• Familiarity with cloud platforms like Azure or AWS, including storage, security, and networking aspects.
• Knowledge of data warehouse design and star/snowflake schema modeling.

Equal Employer/Veterans/Disabled

Navitas Business Consulting is an affirmative action and equal opportunity employer. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Navitas Human Resources.

Navitas is an equal opportunity employer. We provide employment and opportunities for advancement, compensation, training, and growth according to individual merit, without regard to race, color, religion, sex (including pregnancy), national origin, sexual orientation, gender identity or expression, marital status, age, genetic information, disability, veteran-status veteran or military status, or any other characteristic protected under applicable Federal, state, or local law. Our goal is for each staff member to have the opportunity to grow to the limits of their abilities and to achieve personal and organizational objectives. We will support positive programs for equal treatment of all staff and full utilization of all qualified employees at all levels within Navitas.

About Us :

CLOUDSUFI, a Google Cloud Premier Partner, a Data Science and Product Engineering organization building Products and Solutions for Technology and Enterprise industries. We firmly believe in the power of data to transform businesses and make better decisions. We combine unmatched experience in business processes with cutting edge infrastructure and cloud services. We partner with our customers to monetize their data and make enterprise data dance.

Our Values :

We are a passionate and empathetic team that prioritizes human values. Our purpose is to elevate the quality of lives for our family, customers, partners and the community.

Equal Opportunity Statement :

CLOUDSUFI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified candidates receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, and national origin status. We provide equal opportunities in employment, advancement, and all other areas of our workplace.

Role : Architect/Lead-Python(Hands-on experience with at least one SaaS ingestion platform such as Fivetran ,Airbyte,etc. is mandatory.)

Location : Noida, Delhi/NCR(Hybrid)

Experience : 8- 14 years

Education : BTech / BE / MCA / MSc Computer Science

Must have :

- 6+ years of data engineering; at least 3 years working on connector or integration framework development

- Deep Python expertise including PySpark, pyarrow, and an understanding of Spark's execution model (driver vs executor, serialization constraints, partition fan-out)

- Hands-on experience with at least one SaaS ingestion platform Fivetran, Airbyte, Google DTS, AWS Glue connectors, or equivalent at the connector-build level, not just configuration

- Strong understanding of OAuth 2.0 flows (auth code, PKCE, client credentials, JWT), rate limiting strategies (token bucket, leaky bucket, per-endpoint quotas), and incremental sync patterns (cursor, watermark, CDC)

- Experience designing shared connector frameworks reusable auth managers, rate governors, state stores not just per-connector scripts

- Ability to author and own TDDs and PRDs that can be handed to a junior engineer with minimal back-and-forth

Nice to have :

- Prior exposure to Databricks Asset Bundles / Declarative Automation Bundles or Lakeflow pipelines

- Experience with the Databricks Python Data Source API (DBR 15.4 LTS+) extremely rare, so treat practical Spark DSv2 Java/Scala background as equivalent

- GCP DTS or Cloud Data Fusion connector experience

- Knowledge of the specific source systems, particularly Social Ads APIs (Meta, LinkedIn, X) or enterprise SaaS (Salesforce, Oracle)

* 3–5 years of hands-on experience in ETL development, including designing, developing, and optimizing ETL pipelines.

* Strong expertise in T-SQL, SSIS, and SSRS (mandatory skills).

* Strong experience with MS SQL Server for querying, data transformation, and handling large datasets.

* Good understanding of relational and non-relational databases.

* Excellent communication skills with the ability to collaborate with technical and business teams.

Data Engineer at Amura

Amura’s Vision

We believe that the most under-appreciated route to releasing untapped human potential is to build a healthier body, and through which a better brain. This allows us to do more of everything that is important to each one of us. Billions of healthier brains, sitting in healthier bodies, can take up more complex problems that defy solutions today, including many existential threats, and solve them in just a few decades.

Billions of healthier brains will make the world richer beyond what we can imagine today. The surplus wealth, combined with better human capabilities, will lead us to a new renaissance, giving us a richer and more beautiful culture. These healthier brains will be equipped with deeper intellect, be less acrimonious, more magnanimous, and have a kinder outlook on the world, resulting in a world that is better than any previous time.

We find this vision of the future exhilarating. Our hopes and dreams are to create this future as quickly as possible and ensure that it is widely distributed and optimized to maximize all forms of human excellence.

Role Overview

We are looking for a hands-on Data Engineer to design, build, and maintain scalable data pipelines and data platforms. You will work on ingesting, transforming, and serving data reliably for analytics, reporting, and downstream applications, collaborating closely with backend engineers, analysts, and data scientists. This role is ideal for someone who enjoys building robust data systems, working with large datasets, and writing clean, production-grade code.

Key Responsibilities

Data Pipelines & Development

• Build and maintain reliable ETL/ELT pipelines for batch and near-real-time data processing.
• Ingest data from multiple sources (databases, APIs, event streams, files).
• Transform raw data into clean, analytics-ready datasets.
• Optimize pipelines for performance, scalability, and cost.

Data Storage & Modeling

• Design and manage data models in data warehouses or data lakes.
• Work with SQL and NoSQL databases and modern data warehouses.
• Implement partitioning, indexing, and efficient query patterns.
• Maintain documentation for schemas, pipelines, and transformations.

Cloud & Tooling

• Build data solutions on cloud platforms (AWS preferred).
• Use services such as S3, Redshift, Athena, Glue, EMR, Lambda, Kinesis, or equivalents.
• Work with orchestration tools like Airflow or similar schedulers.
• Use version control, CI/CD, and Infrastructure-as-Code where applicable.

Data Quality & Reliability

• Implement data validation, monitoring, and alerting for pipelines.
• Troubleshoot data issues and ensure pipeline reliability.
• Collaborate with stakeholders to resolve data discrepancies.

Collaboration

• Partner with analytics, product, and engineering teams to understand data needs.
• Support analysts and data scientists with clean, accessible datasets.
• Participate in code reviews and contribute to data engineering best practices.

What We’re Looking For

• Experience: 4-6+ years of experience as a Data Engineer / Data Developer.
• Programming: Strong programming skills in Python.
• Databases: Excellent knowledge of SQL and relational data modeling.
• Pipelines: Experience building ETL/ELT pipelines in production.
• Cloud: Hands-on experience with cloud-based data platforms (AWS preferred).
• Concepts: Understanding of data warehousing concepts and best practices.

Nice to Have:

• Experience with Spark, Kafka, dbt, or Flink.
• Familiarity with orchestration tools like Airflow.
• Experience with streaming or event-driven data pipelines.
• Exposure to data quality or observability tools.
• Experience working with large-scale or high-volume datasets.

Additional Information

• Office Location: Chennai (Velachery).
• Work Model: Work from Office - because great stories are built in person!.
• Online Presence: https://amura.ai (@AmuraHealth on all social media).

About LJI

Loyalty Juggernaut (LJI) is a leading B2B SaaS company redefining how enterprises drive customer engagement and loyalty. Our flagship platform, GRAVTY®, enables global brands to transform loyalty programs into measurable, revenue-generating growth engines.

Built as an AI-first, next-generation solution, GRAVTY® empowers organizations to deliver highly personalized, real-time experiences at scale—helping them increase customer lifetime value and deepen brand relationships.

Headquartered in Palo Alto, California, LJI partners with leading enterprises across 16 major industries including airlines, retail, hospitality, financial services and telecommunications powering some of the most innovative loyalty ecosystems worldwide.

Our Global Impact:

• 400+ Million members connected through our platform.
• 100+ Global Brands trust us to drive loyalty and brand devotion.
• 3-Time Winner of “Best Technology Innovation in Loyalty”.
• Global recognitions for Excellence in Loyalty Management under numerous categories.
• Recognised as a ‘Strong performer’ in The Forrester Wave™ Loyalty Platforms, Q4 2025.

Explore more about us at www.lji.io

What you will OWN:

• Build the infrastructure required for optimal extraction, transformation, and loading of data from various sources using SQL and AWS ‘big data’ technologies.
• Create and maintain optimal data pipeline architecture.
• Identify, design, and implement internal process improvements, automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Work with stakeholders, including the Technical Architects, Developers, Product Owners, and Executives, to assist with data-related technical issues and support their data infrastructure needs.
• Create tools for data management and data analytics that can assist them in building and optimizing our product to become an innovative industry leader.

You would make a GREAT FIT if you have:

• Have 2 to 5 years of relevant backend development experience, with solid expertise in Python.
• Possess strong skills in Data Structures and Algorithms, and can write optimized, maintainable code.
• Are familiar with database systems, and can comfortably work with PostgreSQL, as well as NoSQL solutions like MongoDB or DynamoDB.
• Hands-on experience using Cloud Dataware houses like AWS Redshift, GBQ, etc.
• Experience with AWS cloud services: EC2, EMR, RDS, Redshift, and AWS Batch would be an added advantage.
• Have a solid understanding of ETL processes and tools and can build or modify ETL pipelines effectively.
• Have experience managing or building data pipelines and architectures at scale.
• Understand the nuances of data ingestion, transformation, storage, and analytics workflows.
• Communicate clearly and work collaboratively across engineering, product.

Why Choose US?

• This opportunity offers a dynamic and supportive work environment where you'll have the chance to not just collaborate with talented technocrats but also work with globally recognized brands, gain exposure, and carve your own career path.
• You will get to innovate and dabble in the future of technology -Enterprise Cloud Computing, Blockchain, Machine Learning, AI, Mobile, Digital Wallets, and much more.

Sr. DE / Data Engineer (Healthcare Data & SQL Expert)

Experience Level: 5–7 Years

Focus: Database Design, Advanced SQL, ETL/ELT Pipelines, and Healthcare Interoperability.

Summary

We are looking for a highly skilled Senior Data Engineer to join our healthcare data team. This role is perfect for a technical powerhouse who excels at building robust data pipelines and deeply understands database internals. You will be responsible for designing schemas, writing complex stored procedures, and optimizing SQL performance to handle clinical and claims data at scale. You will bridge the gap between raw data ingestion and high-performance analytics, ensuring all solutions meet HIPAA and FHIR standards.

What You’ll Do

1. Advanced SQL & Database Development

• Schema Design: Design and implement relational schemas (MSSQL, PostgreSQL, Oracle) ensuring data integrity through constraints, triggers, and normalized structures.
• Programmability: Write and maintain sophisticated Stored Procedures, Functions, and Views to handle complex business logic within the database layer.
• Performance Tuning: Own query optimization. You should be the expert in reading EXPLAIN/ANALYZE plans, implementing advanced indexing strategies (Clustered, Non-Clustered, Columnstore), and managing partitioning.
• Data Modeling: Build and manage dimensional models (Star/Snowflake) and implement Slowly Changing Dimensions (SCD Types 1, 2, and 4).
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• Getty Images

2. Data Engineering & Ingestion

• Pipeline Development: Build and operate scalable ETL/ELT pipelines using Python and SQL to ingest data from EHRs, REST APIs, and flat files.
• Orchestration: Use Apache Airflow to schedule jobs, manage dependencies, and implement robust retry/alerting logic.
• API Integration: Develop Python-based ingestion frameworks that handle OAuth, pagination, and throttling for third-party healthcare data partners.

3. Healthcare Interoperability & Compliance

• Standards: Map complex clinical data to HL7 FHIR resources and curated analytic layers.
• Security: Implement "Privacy by Design" by enforcing HIPAA safeguards, including encryption at rest, access controls, and PII/PHI de-identification.

4. Operational Excellence

• CI/CD: Use GitHub and automated pipelines to deploy database changes and data code.
• Observability: Implement data quality tests (using tools like dbt or custom Python/SQL checks) to monitor freshness and accuracy.

What You’ll Bring

• Experience: 5–7 years of professional data engineering experience, with a heavy emphasis on backend database development.
• The SQL Expert Toolkit:
• Expert SQL: Window functions, CTEs, recursive queries, and set-based transformations.
• DB Internals: Deep knowledge of MSSQL, PostgreSQL, or Oracle. You should understand how the engine stores and retrieves data.
• Optimization: Proven track record of turning "slow" queries into high-performance assets via indexing and refactoring.
• The Engineering Toolkit:
• Python: Intermediate to advanced (Pandas/Polars, Requests, SQLAlchemy, or PySpark).
• Orchestration: Practical experience with Airflow (or Prefect/Dagster).
• Legacy/Cloud mix: Proficiency in SSIS/SSMA or PowerShell is a plus for migrating legacy workloads to modern platforms.
• The Domain Knowledge: Familiarity with FHIR/HL7 and an understanding of the importance of data governance in a regulated environment.

Technical "Must-Haves" for the Interview

• Ability to whiteboard a complex Database Schema from scratch.
• Ability to debug a long-running SQL query and explain the IO/CPU trade-offs of different index types.
• Experience handling JSON/BSON data types within a relational database context.

Nice to Have

• Experience with NoSQL systems like MongoDB or Elasticsearch.
• Cloud experience (Azure, AWS, or GCP) specifically regarding managed SQL services.
• Knowledge of dbt (data build tool) for managing transformations in the warehouse.

· Strategy & Architecture: Collaborate with stakeholders to define end-to-end migration strategies, including data mapping, transformation, and validation rules.

· Technical Execution: Utilize tools like SQL DB, CSV2TCXML, IPS Upload, and ETL tools to migrate CAD and metadata.

· Customization: Develop custom migration solutions using BMIDE (Business Modeler IDE), ITK (Integration Toolkit), and SOA (Service Oriented Architecture).

· Project Leadership: Break down projects into manageable work packages, leading both onsite and offshore teams.

· Validation & Quality: Perform validation checks to ensure data integrity and accuracy post-migration.

· Integration Support: Manage CAD integrations (NX, Inventor, Creo) and PLM integrations (T4S, T4O, T4EA).

Purpose of the role

To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure. 

Accountabilities

• Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
• Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
• Development of processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaboration with data scientist to build and deploy machine learning models.

Vice President Expectations

• To contribute or set strategy, drive requirements and make recommendations for change. Plan resources, budgets, and policies; manage and maintain policies/ processes; deliver continuous improvements and escalate breaches of policies/procedures..
• If managing a team, they define jobs and responsibilities, planning for the department’s future needs and operations, counselling employees on performance and contributing to employee pay decisions/changes. They may also lead a number of specialists to influence the operations of a department, in alignment with strategic as well as tactical priorities, while balancing short and long term goals and ensuring that budgets and schedules meet corporate requirements..
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others..
• OR for an individual contributor, they will be a subject matter expert within own discipline and will guide technical direction. They will lead collaborative, multi-year assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will train, guide and coach less experienced specialists and provide information affecting long term profits, organisational risks and strategic decisions..
• Advise key stakeholders, including functional leadership teams and senior management on functional and cross functional areas of impact and alignment.
• Manage and mitigate risks through assessment, in support of the control and governance agenda.
• Demonstrate leadership and accountability for managing risk and strengthening controls in relation to the work your team does.
• Demonstrate comprehensive understanding of the organisation functions to contribute to achieving the goals of the business.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategies.
• Create solutions based on sophisticated analytical thought comparing and selecting complex alternatives. In-depth analysis with interpretative thinking will be required to define problems and develop innovative solutions.
• Adopt and include the outcomes of extensive research in problem solving processes.
• Seek out, build and maintain trusting relationships and partnerships with internal and external stakeholders in order to accomplish key business objectives, using influencing and negotiating skills to achieve outcomes.

To be a successful Senior Data Engineer, you should have experience with:

• Hands on experience to work with large scale data platforms & in development of cloud solutions in AWS data platform with proven track record in driving business success.
• Strong understanding of AWS and distributed computing paradigms, ability to design and develop data ingestion programs to process large data sets in Batch mode using Glue, Lambda, S3, redshift and snowflake and data bricks.
• Ability to develop data ingestion programs to ingest real-time data from LIVE sources using Apache Kafka, Spark Streaming and related technologies. Hands on programming experience in python and PY-Spark.
• Understanding of Dev Ops Pipelines using Jenkins, GitLab & should be strong in data modelling and Data architecture concepts & well versed with Project management tools and Agile Methodology.
• Sound knowledge of data governance principles and tools (alation/glue data quality, mesh), Capable of suggesting solution architecture for diverse technology applications.

Additional relevant skills given below are highly valued:

• Experience working in financial services industry & working in various Settlements and Sub ledger functions like PNS, Stock Record and Settlements, PNL.
• Knowledge in BPS, IMPACT & Gloss products from Broadridge & creating ML model using python, Spark & Java.

You may be assessed on key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific technical skills.

Lead / Sr. Data Engineer (Architect & Engineering Owner)

The Role

We are seeking a Lead Data Engineer who operates at the intersection of high-scale engineering and enterprise architecture. In this role, you will "own" our healthcare data platform end-to-end. You aren't just building pipelines; you are designing the blueprint for how clinical, claims, and sales data flow through our ecosystem. You will bridge the gap between legacy systems (MSSQL/Oracle) and modern cloud warehouses (Snowflake/Redshift/Databricks), ensuring our data is governed, HIPAA-compliant, and optimized for advanced analytics.

What You’ll Do

1. Architecture & Strategic Leadership

• Design the Blueprint: Own the enterprise data architecture (Staging, Integration, Warehouse, and Semantic layers). Define the evolution from monolithic databases to scalable cloud-hosted analytics.
• Modeling Mastery: Lead the design of complex Dimensional Models (Star/Snowflake) and implement advanced Slowly Changing Dimension (SCD) strategies to track historical clinical events.
• Set the Standard: Establish coding, version control (GitHub), and CI/CD standards. Conduct design reviews and mentor a team of engineers to move from "task-takers" to "system-builders."

2. Advanced Data Engineering (Hands-on)

• Modern ELT/ETL: Build and orchestrate production-grade pipelines using Python, Airflow, and dbt. Manage automated ingestion via Fivetran or custom-built frameworks for APIs and EHRs.
• Multi-Engine Expertise: Operate seamlessly across PostgreSQL, MSSQL, and Oracle, while optimizing petabyte-scale cloud warehouses like Snowflake or Redshift.
• Performance Tuning: Own query optimization. You should be the expert at using EXPLAIN/ANALYZE, partitioning, and indexing to reduce compute costs and latency.
• Quality & Reconciliation: Design robust validation frameworks to ensure data integrity—essential for healthcare compliance and clinical trust.

3. Healthcare Interoperability & Governance

• Data Standards: Map diverse datasets (EHR, API, Flat Files) to HL7 FHIR resources and curated analytic layers.
• Privacy by Design: Embed HIPAA Security Rule safeguards (encryption, audit trails, and access controls) directly into the code and infrastructure.
• Interoperability: Handle complex semi-structured data (JSON/XML) from third-party partners and EMR systems.

What You’ll Bring

• Experience: 8–12+ years in Data Engineering/Architecture. You should have a track record of leading technical projects or mentoring teams.
• The "Hybrid" Stack: * Expert SQL/PL-SQL: Deep experience with performance tuning in relational environments (Oracle/MSSQL).
• Modern Tools: Practical experience with Snowflake/Redshift, dbt, and Airflow.
• Programming: High proficiency in Python (Pandas, PySpark) or Java/Scala for custom ETL routines.
• Architectural Depth: Clear understanding of SDLC, Agile (Scrum), and Data Modeling frameworks.
• Healthcare Domain: Exposure to pharmaceutical or clinical data (Life Sciences, EMR, or Claims) is highly preferred.
• Soft Skills: The ability to translate "clinical business needs" into "technical runbooks" and communicate effectively with stakeholders.

Nice to Have

• AI/ML Integration: Experience supporting Data Science teams with feature extraction and model deployment (SageMaker/Azure ML).
• Advanced Tooling: Familiarity with NoSQL (MongoDB), search engines (Elasticsearch), or niche ETL tools (Talend/Informatica) for migration purposes.
• Cloud Infrastructure: Hands-on experience with AWS Glue, Lambda, or Azure Data Factory.

Key Skills:

Technical Skills

• Power BI Development: 4-5 years of hands-on experience developing Power BI reports, dashboards, and data models
• DAX: Strong proficiency in DAX (Data Analysis Expressions) for creating measures, calculated columns, and complex calculations
• Power Query / M Language: Expertise in data transformation and ETL processes using Power Query
• Data Modeling: Solid understanding of dimensional modeling, star schema, and data warehouse concepts
• SQL: Proficient in SQL for data extraction, manipulation, and querying relational databases
• Power BI Service: Experience with Power BI Service administration, workspace management, scheduled refreshes, and deployment pipelines
• Custom Visualizations: Experience creating and configuring custom visuals, including use of AppSource visuals and custom visual development using Power BI Visuals SDK
• API Integration: Hands-on experience with Power BI REST APIs for automating deployments, managing workspaces, and embedding reports
• Knowledge of data visualization best practices and UI/UX principles for dashboard design
• Experience with data source connectivity (SQL Server, Azure SQL, Oracle, SAP, Excel, APIs, web services)

Additional Required Qualifications

• Bachelor’s degree in computer science, Information Systems, Business Analytics, or related field
• Strong analytical and problem-solving abilities
• Excellent communication skills to work with both technical and non-technical stakeholders
• Ability to manage multiple projects and prioritize tasks effectively
• Detail-oriented with commitment to delivering high-quality work
• Client-facing experience with ability to gather requirements and present solutions

Preferred Qualifications

• Microsoft Power BI certification (PL-300 or equivalent)
• Experience with Azure ecosystem (Azure Data Factory, Azure Synapse Analytics, Azure SQL Database)
• Knowledge of other Microsoft BI tools (SSRS, SSAS, Excel Power Pivot)
• Familiarity with Python or R for advanced analytics integration
• Experience with Dataflows and incremental refresh strategies
• Understanding of API development for custom visuals or Power BI embedded solutions
• Experience working in Agile/Scrum development environments

JD - 

We are looking for a strong Data Engineer having hands on experience in building pipelines using Snowflake and DBT.

Key Responsibilities:

• Develop, maintain, and optimize data pipelines using DBT and SQL on Snowflake DB.
• Collaborate with data analysts, QA and business teams to build scalable data models.
• Implement data transformations, testing, and documentation within the DBT framework.
• Work on Snowflake for data warehousing tasks, including data ingestion, query optimization, and performance tuning.
• Use Python (preferred) for automation, scripting, and additional data processing as needed.

Required Skills:

• 6+ years of experience in building data engineering pipelines.
• Strong hands-on expertise with DBT and advanced SQL.
• Experience working with modern columnar/MPP data warehouses, preferably Snowflake.
• Knowledge of Python for data manipulation and workflow automation (preferred).
• Good understanding of data modeling concepts, ETL/ELT processes, and best practice.

Job Role: Data Engineer

Location: Mumbai, Andheri.

WFO(Monday-Friday)

Looking for Immediate, 15 days joiner.

Required Skills

• 3+ years of hands-on experience as a Data Engineer.

• Strong proficiency in Python and PySpark programming for data engineering tasks.

• In-depth knowledge of ETL processes and data pipeline architectures. • Experience with Airflow or Step Functions for orchestration and scheduling.

• Solid experience working with AWS services.

• Proficiency in building and maintaining CI/CD pipelines

. • Experience with data modelling, database design, and querying.

• Strong problem-solving skills, with the ability to troubleshoot and optimize complex data pipelines.

Bonus Skills

• Knowledge of cloud platforms (e.g., AWS, GCP).

• Experience with containerization and Kubernetes.

• Experience in data security, encryption, and compliance best practices.

• Strong communication and collaboration skills. 

Responsibilities

• Design, build, and maintain scalable and efficient ETL/ELT pipelines to process large-scale datasets.

• Implement and manage workflow automation and orchestration using Apache Airflow or Step Functions.

• Build and optimize data infrastructure using AWS services.

• Integrate and manage data warehouses.

• Design and develop dynamic and interactive dashboards using PowerBI, Metabase, and Superset, to present insights.

• Develop and maintain CI/CD pipelines for seamless deployment and continuous integration of data solutions.

• Monitor and troubleshoot data pipeline issues, ensuring data quality and consistency.

• Leverage best practices for data governance, security, and compliance in cloud environments.

• Collaborate with data scientists, analysts, and other stakeholders to understand data needs and deliver reliable data solutions. 

Job Title: Integration Engineer

Integration Engineers are responsible for defining, developing, delivering, maintaining and supporting end-to-end Enterprise Integration solutions. Using a designated IPaaS solution (e.g. Boomi), Integration Engineers integrate multiple cloud and on-premise applications which help customers publish and consume data between Oddr and third party systems for a variety of tasks.

Job Summary:

We are seeking a skilled and experienced Integration Engineer to join our Technology team in India. The ideal candidate will have a strong background in implementing low-code/no-code integration platforms as a service (iPaaS), with a preference for experience in Boomi. The role requires an in-depth understanding of SQL and RESTful APIs. Experience with Intapp's Integration Builder is a significant plus.

Key Responsibilities:

- Design and implement integration solutions using iPaaS tools.

- Collaborate with customers, product, engineering and business stakeholders to translate business requirements into robust and scalable integration processes.

- Develop and maintain SQL queries and scripts to facilitate data manipulation and integration.

- Utilize RESTful API design and consumption to ensure seamless data flow between various systems and applications.

- Lead the configuration, deployment, and ongoing management of integration projects.

- Troubleshoot and resolve technical issues related to integration solutions.

- Document integration processes and create user guides for internal and external users.

- Stay current with the latest developments in iPaaS technologies and best practices.

Qualifications:

- Bachelor’s degree in Computer Science, Information Technology, or a related field.

- Minimum of 2 years’ experience in an integration engineering role with hands-on experience in an iPaaS tool, preferably Boomi.

- Proficiency in SQL and experience with database management and data integration patterns.

- Strong understanding of integration patterns and solutions, API design, and cloud-based technologies.

- Good understanding of RESTful APIs and integration.

- Excellent problem-solving and analytical skills.

- Strong communication and interpersonal skills, with the ability to work effectively in a team environment.

- Experience with various integration protocols (REST, SOAP, FTP, etc.) and data formats (JSON, XML, etc.).

Preferred Skills:

- Boomi (or other iPaaS) certifications

- Experience with Intapp's Integration Builder is highly desirable but not mandatory.

- SQL Knowledge is important

- Experience in building E2E integrations and communicating with stakeholders

- Knowledge of Azure Functions, LogicApps, And other Azure Services is highly desirable

What we offer:

- Competitive salary and benefits package.

- Dynamic and innovative work environment.

- Opportunities for professional growth and advancement.

We are seeking a skilled Data Engineer to join the AI Platform Capabilities team supporting the UDP Uplift program.

In this role, you will design, build, and test standardized data and AI platform capabilities across a multi-cloud environment (Azure & GCP).

You will collaborate closely with AI use case teams to develop:

• Scalable data pipelines
• Reusable data products
• Foundational data infrastructure

Your work will support advanced AI solutions such as:

• GenAI
• RAG (Retrieval-Augmented Generation)
• Document Intelligence

Key Responsibilities

• Design and develop scalable ETL/ELT pipelines for AI workloads
• Build and optimize data pipelines for structured & unstructured data
• Enable context processing & vector store integrations
• Support streaming data workflows and batch processing
• Ensure adherence to enterprise data models, governance, and security standards
• Collaborate with DataOps, MLOps, Security, and business teams (LBUs)
• Contribute to data lifecycle management for AI platforms

Required Skills

• 5–7 years of hands-on experience in Data Engineering
• Strong expertise in Python and advanced SQL
• Experience with GCP and/or Azure cloud-native data services
• Hands-on experience with PySpark / Spark SQL
• Experience building data pipelines for ML/AI workloads
• Understanding of CI/CD, Git, and Agile methodologies
• Knowledge of data quality, governance, and security practices
• Strong collaboration and stakeholder management skills

Nice-to-Have Skills

• Experience with Vector Databases / Vector Stores (for RAG pipelines)
• Familiarity with MLOps / GenAIOps concepts (feature stores, model registries, prompt management)
• Exposure to Knowledge Graphs / Context Stores / Document Intelligence workflows
• Experience with DBT (Data Build Tool)
• Knowledge of Infrastructure-as-Code (Terraform)
• Experience in multi-cloud deployments (Azure + GCP)
• Familiarity with event-driven systems (Kafka, Pub/Sub) & API integrations

Ideal Candidate Profile

• Strong data engineering foundation with AI/ML exposure
• Experience working in multi-cloud environments
• Ability to build production-grade, scalable data systems
• Comfortable working in cross-functional, fast-paced environments

Key Responsibilities

 Architect and implement enterprise-grade Lakehouse solutions using Databricks

 Design and deliver scalable batch and real-time data pipelines using Apache Spark (PySpark/SQL)

 Build ETL/ELT pipelines, incremental data loads, and metadata-driven ingestion frameworks

 Implement and optimize Databricks components: Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, and Workflows

 Design large-scale data warehousing solutions with 3NF and dimensional modeling

 Establish data governance, security, and data quality frameworks, including Unity Catalog

 Lead ML lifecycle management using MLflow and drive AI use cases (RAG, AI/BI)

 Manage cloud-native deployments on Microsoft Azure and integrate with enterprise systems (e.g., ServiceNow)

 Drive CI/CD, DevOps practices, and performance optimization of Spark workloads

 Provide technical leadership, mentor teams, and ensure successful delivery

 Collaborate with stakeholders to translate business requirements into scalable solutions

Required Skills & Experience

 10+ years in Data Engineering / Analytics / AI with strong delivery ownership

 Deep expertise in Databricks ecosystem (Notebooks, Delta Lake, Workflows, AI/BI, Apps, Genie)

 Strong hands-on experience with: 

a. Apache Spark (performance tuning & scalability)

b. Python and SQL

 Proven experience in: 

a. Solution architecture and large-scale data platforms

b. Data warehousing and advanced data modeling

c. Batch and real-time processing systems

 Experience with: 

a. Azure Databricks and Azure data services

b. MLflow and MLOps practices

c. ServiceNow or enterprise integrations

 Exposure to AI technologies (RAG, LLM-based solutions)

 Strong stakeholder management and leadership skills

Certifications (Preferred)

 Databricks certifications aligned to data engineering and AI tracks, such as: 

a. Databricks Certified Data Engineer Associate (validates foundational ETL, Spark, and Lakehouse capabilities)

b. Databricks Certified Data Engineer Professional (advanced expertise in pipeline design, optimization, and governance)

 Certifications in Databricks Machine Learning or Generative AI tracks (e.g., ML Associate / Professional) for AI-driven use cases

 Relevant cloud certifications in Microsoft Azure or Amazon Web Services for platform deployment and architecture

Profile - Databricks Developer

Experience- 5+ years

Location- Bangalore (On site)

PF & BGV is Mandatory

Job Description: -

* Design, build, and optimize data pipelines and ETL/ELT workflows using Databricks and

Apache Spark (PySpark).

* Develop scalable, high performance data solutions using Spark distributed processing.

* Lead engineering initiatives focused on automation, performance tuning, and platform

modernization.

* Implement and manage CI/CD pipelines using Git-based workflows and tools such as

GitHub Actions or Jenkins.

* Collaborate with cross-functional teams to translate business needs into technical

solutions.

* Ensure data quality, governance, and security across all processes.

* Troubleshoot and optimize Spark jobs, Databricks clusters, and workflows.

* Participate in code reviews and develop reusable engineering frameworks.

* Should have knowledge of utilizing AI tools to improve productivity and support daily

engineering activities.

* Strong knowledge and hands-on experience in Databricks Genie, including prompt

engineering, workspace usage, and automation.

Required Skills & Experience:

* 5+ years of experience in Data Engineering or related fields.

* Strong hands-on expertise in Databricks (notebooks, Delta Lake, job orchestration).

* Deep knowledge of Apache Spark (PySpark, Spark SQL, optimization techniques).

* Strong proficiency in Python for data processing, automation, and framework

development.

* Strong proficiency in SQL, including complex queries, performance tuning, and analytical

functions.

* Strong knowledge of Databricks Genie and leveraging it for engineering workflows.

* Strong experience with CI/CD and Git-based development workflows.

* Proficiency in data modeling and ETL/ELT pipeline design.

* Experience with automation frameworks and scheduling tools.

* Solid understanding of distributed systems and big data concepts

Job Summary

We are seeking a skilled Data Engineer with 4+ years of experience in building scalable data pipelines and working with modern data platforms. The ideal candidate should have strong expertise in Python, SQL, and cloud-based data solutions, with hands-on experience in ETL/ELT processes and data warehousing.

Key Responsibilities

• Design, build, and maintain scalable data pipelines using Python
• Develop and optimize ETL/ELT workflows for data ingestion and transformation
• Work with structured and unstructured data from multiple sources
• Build and manage data warehouses/data lakes
• Perform data validation, cleansing, and quality checks
• Optimize SQL queries and improve data processing performance
• Collaborate with data analysts, data scientists, and business teams
• Implement data governance, security, and best practices
• Monitor pipelines and troubleshoot production issues

Required Skills

• Strong programming experience in Python (Pandas, NumPy, PySpark preferred)
• Excellent SQL skills (joins, window functions, performance tuning)
• Experience with ETL tools like Informatica, Talend, or DBT
• Hands-on experience with cloud platforms (Azure / AWS / GCP)
• Experience in data warehousing solutions like Snowflake, Redshift, BigQuery
• Knowledge of workflow orchestration tools like Apache Airflow
• Familiarity with version control tools like Git

Preferred Skills

• Experience with Big Data technologies (Spark, Hadoop)
• Knowledge of streaming tools like Kafka
• Exposure to CI/CD pipelines and DevOps practices
• Experience in data modeling (Star/Snowflake schema)
• Understanding of APIs and data integration

The AI Data Engineer will be responsible for designing, building, and operating scalable data pipelines and curated data assets that power machine learning, generative AI, and intelligent automation solutions in an SLA-driven managed services environment. This role focuses on data ingestion, transformation, governance, and operational reliability across cloud and hybrid environments enabling use cases such as knowledge retrieval (RAG), conversational AI, predictive analytics, and AI-assisted service management. The ideal candidate combines strong data engineering fundamentals with an understanding of AI workload requirements, including quality, lineage, privacy, and performance. 

Key Responsibilities 

•Design, build, and operate production-grade data pipelines that support AI/ML and generative AI workloads in managed services environments 

•Develop curated, analytics-ready datasets and data products to enable model training, grounding, feature generation, and AI search/retrieval 

•Implement data ingestion patterns for structured and unstructured sources (APIs, databases, files, event streams, documents) 

•Build and maintain transformation workflows with strong testing and validation 

•Enable Retrieval-Augmented Generation (RAG) by preparing document corpora, chunking strategies, metadata enrichment, and vector indexing patterns 

•Integrate data pipelines with application services 

•Support ITSM and enterprise workflow data needs, including ServiceNow data integration, CMDB/incident data quality improvements, and automation enablement 

•Implement observability for data pipelines (monitoring, alerting, SLAs/SLOs) and perform root cause analysis for pipeline failures or data quality incidents 

•Apply data governance and security best practices 

•Collaborate with ML Engineers, DevOps/SRE, and solution architects to operationalize end-to-end AI solutions 

•Contribute to reusable patterns, templates, and standards within the Bell Techlogix AI Center of Excellence 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience 

•5+ years of experience in data engineering, analytics engineering, or platform data operations 

•Strong proficiency in SQL and Python; experience with data modeling and dimensional concepts 

•Hands-on experience with Azure data services (e.g., Data Factory, Synapse, Databricks, Storage, Key Vault) or equivalent cloud tooling 

•Experience building reliable pipelines with scheduling, dependency management, and automated testing/validation 

•Experience supporting production data platforms with incident management, troubleshooting, and root cause analysis 

•Understanding of data security, privacy, and governance principles in enterprise environments 

Preferred Qualifications 

•Experience enabling AI/ML workloads: feature engineering, training data preparation, and integration with Azure Machine Learning 

•Experience with unstructured data processing for generative AI 

•Familiarity with vector databases or vector search and RAG patterns 

•Experience with event streaming and messaging 

•Familiarity with ServiceNow data model and integration patterns (Table API, export, CMDB/ITSM reporting) 

•Relevant certifications (Microsoft Azure Data Engineer, Azure AI Engineer, Databricks) 

Profile - Databricks Developer

Experience- 5+ years

Location- Bangalore (On site)

PF & BGV is Mandatory

Job Description: -

* Design, build, and optimize data pipelines and ETL/ELT workflows using Databricks and Apache Spark (PySpark).

* Develop scalable, high performance data solutions using Spark distributed processing.

* Lead engineering initiatives focused on automation, performance tuning, and platform modernization.

* Implement and manage CI/CD pipelines using Git-based workflows and tools such as GitHub Actions or Jenkins.

* Collaborate with cross-functional teams to translate business needs into technical solutions.

* Ensure data quality, governance, and security across all processes.

* Troubleshoot and optimize Spark jobs, Databricks clusters, and workflows.

* Participate in code reviews and develop reusable engineering frameworks.

* Should have knowledge of utilizing AI tools to improve productivity and support daily engineering activities.

* Strong knowledge and hands-on experience in Databricks Genie, including prompt engineering, workspace usage, and automation

. Required Skills & Experience:

* 5+ years of experience in Data Engineering or related fields.

* Strong hands-on expertise in Databricks (notebooks, Delta Lake, job orchestration).

* Deep knowledge of Apache Spark (PySpark, Spark SQL, optimization techniques).

* Strong proficiency in Python for data processing, automation, and framework development.

* Strong proficiency in SQL, including complex queries, performance tuning, and analytical functions.

* Strong knowledge of Databricks Genie and leveraging it for engineering workflows.

* Strong experience with CI/CD and Git-based development workflows. * Proficiency in data modeling and ETL/ELT pipeline design.

* Experience with automation frameworks and scheduling tools.

* Solid understanding of distributed systems and big data concepts

Who are we ?

Searce means ‘a fine sieve’ & indicates ‘to refine, to analyze, to improve’. It signifies our way of working: To improve to the finest degree of excellence, ‘solving for better’ every time. Searcians are passionate improvers & solvers who love to question the status quo.

The primary purpose of all of us, at Searce, is driving intelligent, impactful & futuristic business outcomes using new-age technology. This purpose is driven passionately by HAPPIER people who aim to become better, everyday.

Tech Superpowers

End-to-End Ecosystem Thinker: You build modular, reusable data products across ingestion, transformation (ETL/ELT), and consumption layers. You ensure the entire data lifecycle is governed, scalable, and optimized for high-velocity delivery.

The MDS Architect. You reimagine business with the Modern Data Stack (MDS) to deliver Data Mesh implementations and real value. You treat every dataset as a measurable "Data Product with a clear focus on ROI and time-to-insight.

Distributed Compute & Scale Savant: You craft resilient architectures that survive petabyte scale volume and data skew without "breaking the bank. You prove your designs with cost-performance benchmarks, not just slideware.

Al-Ready Orchestrator: You engineer the bridge between structured data and Unstructured/Vector stores. By mastering pipelines for RAG models and GenAl, you turn raw data into the fuel for intelligent, automated workflows.

The Quality Craftsman (Builder @ Heart): You are an outcome-focused leader who lives in the code. From embedding GDPR/PII privacy-by-design to optimizing SQL, Python, and Spark daily, you ensure integrity is baked into every table

Experience & Relevance

Engineering Depth: 7-10 years of professional experience in end-to-end data product development. You have a portfolio that proves your ability to build complex, high-velocity pipelines for both Batch and Streaming workloads

Cloud-Native Fluency: Deep, hands-on experience designing and deploying scalable data solutions on at least one major cloud platform (AWS, GCP, or Azure). You are comfortable navigating the nuances of EMR, BigQuery, or Synapse at scale.

Al-Native Workflow: You don't just build for Al you build with Al. You must be proficient in using Al coding assistants (e.g.. GitHub Copilot) to accelerate your delivery and have a track record of building the data foundations required for Generative Al.

Architectural Portfolio: Evidence of leading 2-3 large-scale transformations-including platform migrations, data lakehouse builds, or real-time

analytics architectures.

Client-Facing Acumen: You have direct experience in a consultative, client-facing role. You can confidently translate a CEO's business vision into a Lead Engineer's technical specification without losing anything in translation.

The "Solver" Mindset: A track record of solving 'impossible data problems-whether it's fixing massive data skew, optimizing spiraling cloud costs, or architecting 99.9% available data services.

Job Title: Lead Data Architect (AI & Cloud)

Company: Risosu Consulting

About the Role

Risosu Consulting is hiring a Lead Data Architect / Crew Manager for one of our global clients in the Cloud, Data & AI space. This role focuses on designing scalable data architectures and driving AI-led transformation across modern cloud platforms.

Key Responsibilities

• Design data strategies, architectures, and scalable cloud solutions
• Build and optimize data pipelines, data lakes, and warehouses
• Collaborate with cross-functional teams to enable AI/ML use cases
• Lead client engagements and translate business needs into data solutions
• Mentor and manage a team of consultants as a Crew Manager

Requirements

• 5+ years of experience in Data Architecture / Engineering
• Strong expertise in cloud platforms (GCP/AWS/Azure)
• Experience with data modeling, ETL, and data governance
• Exposure to tools like BigQuery, dbt, Airbyte, or Power BI
• Strong communication skills and stakeholder management

Why Join via Risosu?

• Opportunity to work on high-impact global projects
• Fast-growing, entrepreneurial environment
• Clear growth path with learning & certification support
• Work with cutting-edge Cloud, Data & AI technologies

If you’re passionate about building scalable data systems and leading teams, let’s connect.

Lead Data Engineer

What are we looking for

real solver?

Solver? Absolutely. But not the usual kind. We're searching for the architects of the audacious & the pioneers of the possible. If you're the type to dismantle assumptions, re-engineer ‘best practices,’ and build solutions that make the future possible NOW, then you're speaking our language.

Your Responsibilities

What you will wake up to solve.

• Lead Technical Design & Data Architecture: Architect and lead the end-to-end development of scalable, cloud-native data platforms. You’ll guide the squad on critical architectural decisions—choosing between Batch vs. Streaming or ETL vs. ELT—while remaining 100% hands-on, contributing high-quality, production-grade code.
• Build High-Velocity Data Pipelines: Drive the implementation of robust data transports and ingestion frameworks using Python, SQL, and Spark. You will build integration layers that connect heterogeneous sources (SaaS, RDBMS, NoSQL) into unified, high-availability environments like BigQuery, Snowflake, or Redshift.
• Mentor & Elevate the Squad: Foster a culture of technical excellence by mentoring and inspiring a team of data analysts and engineers. Lead deep-dive code reviews, promote best-practice data modeling (Star/Snowflake schema), and ensure the squad adopts modern engineering standards like CI/CD for data.
• Drive AI-Ready Data Strategy: Be the expert in designing data foundations optimized for AI and Machine Learning. You will champion the use of GCP (Dataflow, Pub/Sub, BigQuery) and AWS (Lambda, Glue, EMR) to create "clean room" environments that fuel advanced analytics and generative AI models.
• Partner with Clients as a Technical DRI: Act as the Directly Responsible Individual for client success. Translate ambiguous business questions into elegant data services, manage project deliverables using Agile methodologies, and ensure that the data provided is accurate, consistent, and mission-critical.
• Troubleshoot & Optimize for Scale: Own the reliability of the reporting layer. You will proactively monitor pipelines, troubleshoot complex transformation bottlenecks, and propose ways to improve platform performance and cost-efficiency.
• Innovate and Build Reusable IP: Spearhead the creation of reusable data frameworks, custom operators, and transformation libraries that accelerate future projects and establish Searce’s unique technical advantage in the market.

Welcome to Searce

The AI-Native tech consultancy that's rewriting the rules.

Searce is an AI-native, engineering-led, modern tech consultancy that empowers clients to futurify their business by delivering intelligent, impactful, real business outcomes. Searce solvers co-innovate with clients as their trusted transformational partners ensuring sustained competitive advantage. Searce clients realize smarter, faster, better business outcomes delivered by AI-native Searce solver squads. 

Functional Skills 

the solver personas.

• The Data Architect: This persona deconstructs ambiguous business goals into scalable, elegant data blueprints. They don't just move data; they design the foundation—from schema design to partitioning strategies—that allows data scientists and analysts to thrive, foreseeing technical bottlenecks and making pragmatic trade-offs.
• The Player-Coach: As a hands-on leader, this persona leads from the front by writing exemplary, production-grade SQL and Python while simultaneously mentoring and elevating the skills of the squad. Their success is measured by the team's ability to deliver high-quality, maintainable code and their growth as engineers.
• The Pragmatic Innovator: This individual balances a passion for modern data tech (like Generative AI and Real-time Streaming) with a sharp focus on business outcomes. They champion new tools where they add real value but are disciplined enough to choose stable, cost-effective solutions to meet deadlines and deliver robust products.
• The Client-Facing Technologist: This persona acts as the crucial technical bridge between the data squad and the client. They build trust by listening actively, explaining complex data concepts (like data latency or idempotency) in simple terms, and demonstrating how engineering decisions align with the client’s strategic goals.
• The Quality Craftsman: This individual possesses an unwavering commitment to data integrity and treats data engineering as a craft. They are the guardian of the reporting layer, advocating for robust testing, data validation frameworks, and clean, modular code to ensure the long-term reliability of the data platform.

Experience & Relevance 

• Engineering Depth: 7-10 years of professional experience in end-to-end data product development. You have a portfolio that proves your ability to build complex, high-velocity pipelines for both Batch and Streaming workloads.
• Cloud-Native Fluency: Deep, hands-on experience designing and deploying scalable data solutions on at least one major cloud platform (AWS, GCP, or Azure). You are comfortable navigating the nuances of EMR, BigQuery, or Synapse at scale.
• AI-Native Workflow: You don’t just build for AI; you build with AI. You must be proficient in using AI coding assistants (e.g., GitHub Copilot) to accelerate your delivery and have a track record of building the data foundations required for Generative AI.
• Architectural Portfolio: Evidence of leading 2-3 large-scale transformations—including platform migrations, data lakehouse builds, or real-time analytics architectures.
• Client-Facing Acumen: You have direct experience in a consultative, client-facing role. You can confidently translate a CEO’s business vision into a Lead Engineer’s technical specification without losing anything in translation.

Join the ‘real solvers’

ready to futurify?

If you are excited by the possibilities of what an AI-native engineering-led, modern tech consultancy can do to futurify businesses, apply here and experience the ‘Art of the possible’. Don’t Just Send a Resume. Send a Statement.

Solutions Architect - Data Engineering

Modern tech solutions advisory & 'futurify' consulting as a Searce lead fds (‘forward deployed solver’) architecting scalable data platforms and robust data engineering solutions that power intelligent insights and fuel AI innovation.

If you’re a tech-savvy, consultative seller with the brain of a strategist, the heart of a builder, and the charisma of a storyteller — we’ve got a seat for you at the front of the table.

You're not a sales lead. You're the transformation driver.

What are we looking for

real solver?

Solver? Absolutely. But not the usual kind. We're searching for the architects of the audacious & the pioneers of the possible. If you're the type to dismantle assumptions, re-engineer ‘best practices,’ and build solutions that make the future possible NOW, then you're speaking our language.

• Improver. Solver. Futurist.
• Great sense of humor.
• ‘Possible. It is.’ Mindset.
• Compassionate collaborator. Bold experimenter. Tireless iterator.
• Natural creativity that doesn’t just challenge the norm, but solves to design what’s better.
• Thinks in systems. Solves at scale.

This Isn’t for Everyone. But if you’re the kind who questions why things are done a certain way— and then identifies 3 better ways to do it — we’d love to chat with you.

Your Responsibilities

what you will wake up to solve.

You are not just a Solutions Architect; you are a futurifier of our data universe and the primary enabler of our AI ambitions. With a deep-seated passion for data engineering, you will architect and build the foundational data infrastructure that powers the customers entire data intelligence ecosystem.

As the Directly Responsible Individual (DRI) for our enterprise-grade data platforms, you own the outcome, end-to-end. You are the definitive solver for our customer's most complex data challenges, leveraging a powerful tech stack including Snowflake, Databricks, etc. and core GCP & AWS services (BigQuery, Spanner, Airflow, Kafka). This is a hands-on-keys role where you won't just design solutions—you'll build them, break them, and perfect them.

• Solution Design & Pre-sales Excellence:Collaborate with cross-functional teams, including sales, engineering, and operations, to ensure successful project delivery.
• Design Core Data Engineering: Master data modeling, architecting high-performance data ingestion pipelines and ensuring data quality and governance throughout the data lifecycle.
• Enable Cloud & AI: Design and implement solutions utilizing core GCP data services, building foundational data platforms that efficiently support advanced analytics and AI/ML initiatives.
• Optimize Performance & Cost: Continuously optimize data architectures and implementations for performance, efficiency, and cost-effectiveness within the cloud environment.
• Bridge Business & Tech: Translate complex business requirements into clear technical designs, providing technical leadership and guidance to data engineering teams.
• Stay Ahead of the Curve: Continuously research and evaluate new data technologies, architectural patterns, and industry trends to keep our data platforms at the cutting edge.

Functional Skills:

• Enterprise Data Architecture Design: Expert ability to design holistic, scalable, and resilient data architectures for complex enterprise environments.
• Cloud Data Platform Strategy: Proven capability to strategize, design, and implement cloud-native data platforms.
• Pre-Sales & Technical Storyteller: Crafts compelling, client-ready proposals, architectural decks, and technical demonstrations. Doesn't just present; shapes the strategic technical narrative behind every proposed solution.
• Advanced Data Modelling: Mastery in designing various data models for analytical, operational, and transactional use cases.
• Data Ingestion & Pipeline Orchestration: Strong expertise in designing and optimizing robust data ingestion and transformation pipelines.
• Stakeholder Communication: Exceptional skills in articulating complex technical concepts and architectural decisions to both technical and non-technical stakeholders.
• Performance & Cost Optimization: Adept at optimizing data solutions for performance, efficiency, and cost within a cloud environment.

Tech Superpowers:

• Cloud Data Mastery: You're a wizard at leveraging public cloud data services, with deep expertise in GCP (BigQuery, Spanner, etc.) and expert proficiency in modern data warehouse solutions like Snowflake.
• Data Engineering Core: Highly skilled in designing, implementing, and managing data workflows using tools like Apache Airflow and Apache Kafka. You're also an authority on advanced data modeling and ETL/ELT patterns.
• AI/ML Data Foundation: You instinctively design data pipelines and structures that efficiently feed and empower Machine Learning and Artificial Intelligence applications.
• Programming for Data: You have a strong command over key programming languages (Python, SQL) for scripting, automation, and building data processing applications.

Experience & Relevance:

• Architectural Leadership (8+ Years): You bring extensive experience (7+ years) specifically in a Solutions Architect role, focused on data engineering and platform building.
• Cloud Data Expertise: You have a proven track record of designing and implementing production-grade data solutions leveraging major public cloud platforms, with significant experience in Google Cloud Platform (GCP).
• Data Warehousing & Data Platform: Demonstrated hands-on experience in the end-to-end design, implementation, and optimization of modern data warehouses and comprehensive data platforms.
• Databricks & BigQuery Mastery: You possess significant practical experience with Databricks as a core data warehouse and GCP BigQuery for analytical workloads.
• Data Ingestion & Orchestration: Proven experience designing and implementing complex data ingestion pipelines and workflow orchestration using tools like Airflow and real-time streaming technologies like Kafka.
• AI/ML Data Enablement: Experience in building data foundations specifically geared towards supporting Machine Learning and Artificial Intelligence initiatives.

Join the ‘real solvers’

ready to futurify?

If you are excited by the possibilities of what an AI-native engineering-led, modern tech consultancy can do to futurify businesses, apply here and experience the ‘Art of the possible’.

Don’t Just Send a Resume. Send a Statement.

So, If you are passionate about tech, future & what you read above (we really are!), apply here to experience the ‘Art of Possible’

Job Title : AWS Data Engineer

Experience : 4+ Years

Location : Bengaluru (HSR – Hybrid, 3 Days WFO)

Notice Period : Immediate Joiner

💡 Role Overview :

We are looking for a skilled AWS Data Engineer to design, build, and scale modern data platforms. The role involves working with AWS-native services, Python, Spark, and DBT to deliver secure, scalable, and high-performance data solutions in an Agile environment.

🔥 Mandatory Skills :

Python, SQL, Spark, AWS (S3, Glue, EMR, Redshift, Athena, Lambda), DBT, ETL/ELT pipeline development, Airflow/Step Functions, Data Lake (Parquet/ORC/Iceberg), Terraform & CI/CD, Data Governance & Security

🚀 Key Responsibilities :

• Design, build, and optimize ETL/ELT pipelines using Python, DBT, and AWS services
• Develop and manage scalable data lakes on S3 using formats like Parquet, ORC, and Iceberg
• Build end-to-end data solutions using Glue, EMR, Lambda, Redshift, and Athena
• Implement data governance, security, and metadata management using Glue Data Catalog, Lake Formation, IAM, and KMS
• Orchestrate workflows using Airflow, Step Functions, or AWS-native tools
• Ensure reliability and automation via CloudWatch, CloudTrail, CodePipeline, and Terraform
• Collaborate with data analysts and data scientists to deliver actionable insights
• Work in an Agile environment to deliver high-quality data solutions

✅ Mandatory Skills :

• Strong Python (including AWS SDKs), SQL, Spark
• Hands-on experience with AWS data stack (S3, Glue, EMR, Redshift, Athena, Lambda)
• Experience with DBT and ETL/ELT pipeline development
• Workflow orchestration using Airflow / Step Functions
• Knowledge of data lake formats (Parquet, ORC, Iceberg)
• Exposure to DevOps practices (Terraform, CI/CD)
• Strong understanding of data governance and security best practices
• Minimum 4–7 years in Data Engineering (3+ years on AWS)

➕ Good to Have :

• Understanding of Data Mesh architecture
• Experience with platforms like Data.World
• Exposure to Hadoop / HDFS ecosystems

🤝 What We’re Looking For :

• Strong problem-solving and analytical skills
• Ability to work in a collaborative, cross-functional environment
• Good communication and stakeholder management skills
• Self-driven and adaptable to fast-paced environments

📝 Interview Process :

1. Online Assessment
2. Technical Interview
3. Fitment Round
4. Client Round

Job Title: Data Developer Lead

Experience: 10+ Years

Location: Gurgaon (Onsite)

Work Model: Hybrid

Role Overview:

We are looking for a highly experienced Data Developer with 10+ years of expertise in designing, developing, and managing scalable data solutions. The ideal candidate will have strong experience in data engineering, ETL processes, and database management, with the ability to work in a hybrid onsite environment in Gurgaon.

Key Responsibilities:

• Design, develop, and maintain robust data pipelines and ETL processes
• Build scalable data architectures and optimize data workflows
• Work with large datasets to ensure data accuracy, integrity, and availability
• Develop and manage data warehouses and data lakes
• Collaborate with cross-functional teams including Data Science, Analytics, and Engineering
• Optimize database performance and troubleshoot data-related issues
• Ensure data security, governance, and compliance standards are followed
• Automate data processes and improve system efficiency
• Mentor junior team members and provide technical guidance

Required Skills & Qualifications:

• 10+ years of experience in Data Engineering / Data Development
• Strong proficiency in SQL and database technologies (e.g., PostgreSQL, MySQL, Oracle)
• Hands-on experience with ETL tools (Informatica, Talend, Apache NiFi, etc.)
• Experience with big data technologies (Hadoop, Spark, Hive)
• Proficiency in programming languages like Python, Java, or Scala
• Experience with cloud platforms (AWS, Azure, or GCP)
• Strong understanding of data warehousing concepts (Snowflake, Redshift, BigQuery, etc.)
• Experience with data modeling and schema design
• Familiarity with workflow orchestration tools (Airflow, etc.)

Good to Have:

• Experience in real-time data streaming (Kafka, Flink)
• Exposure to DevOps practices and CI/CD pipelines
• Knowledge of data governance and data quality frameworks
• Experience working in Agile environments

Soft Skills:

• Strong problem-solving and analytical skills
• Excellent communication and stakeholder management
• Ability to work independently and in a collaborative team

Why Join Us?

• Opportunity to work on large-scale data systems
• Collaborative and innovation-driven environment
• Flexible hybrid work model

We are looking for a skilled Data Engineer / Data Warehouse Engineer to design, develop, and maintain scalable data pipelines and enterprise data warehouse solutions. The role involves close collaboration with business stakeholders and BI teams to deliver high-quality data for analytics and reporting.

Key Responsibilities

• Collaborate with business users and stakeholders to understand business processes and data requirements
• Design and implement dimensional data models, including fact and dimension tables
• Identify, design, and implement data transformation and cleansing logic
• Build and maintain scalable, reliable, and high-performance ETL/ELT pipelines
• Extract, transform, and load data from multiple source systems into the Enterprise Data Warehouse
• Develop conceptual, logical, and physical data models, including metadata, data lineage, and technical definitions
• Design, develop, and maintain ETL workflows and mappings using appropriate data load techniques
• Provide high-level design, research, and effort estimates for data integration initiatives
• Provide production support for ETL processes to ensure data availability and SLA adherence
• Analyze and resolve data pipeline and performance issues
• Partner with BI teams to design and develop reports and dashboards while ensuring data integrity and quality
• Translate business requirements into well-defined technical data specifications
• Work with data from ERP, CRM, HRIS, and other transactional systems for analytics and reporting
• Define and document BI usage through use cases, prototypes, testing, and deployment
• Support and enhance data governance and data quality processes
• Identify trends, patterns, anomalies, and data quality issues, and recommend improvements
• Train and support business users, IT analysts, and developers
• Lead and collaborate with teams spread across multiple locations

Required Skills & Qualifications

• Bachelor’s degree in Computer Science or a related field, or equivalent work experience
• 3+ years of experience in Data Warehousing, Data Engineering, or Data Integration
• Strong expertise in data warehousing concepts, tools, and best practices
• Excellent SQL skills
• Strong knowledge of relational databases such as SQL Server, PostgreSQL, and MySQL
• Hands-on experience with Google Cloud Platform (GCP) services, including:

1. BigQuery
2. Cloud SQL
3. Cloud Composer (Airflow)
4. Dataflow
5. Dataproc
6. Cloud Functions
7. Google Cloud Storage (GCS)

• Experience with Informatica PowerExchange for Mainframe, Salesforce, and modern data sources
• Strong experience integrating data using APIs, XML, JSON, and similar formats
• In-depth understanding of OLAP, ETL frameworks, Data Warehousing, and Data Lakes
• Solid understanding of SDLC, Agile, and Scrum methodologies
• Strong problem-solving, multitasking, and organizational skills
• Experience handling large-scale datasets and database design
• Strong verbal and written communication skills
• Experience leading teams across multiple locations

Good to Have

• Experience with SSRS and SSIS
• Exposure to AWS and/or Azure cloud platforms
• Experience working with enterprise BI and analytics tools

Why Join Us

• Opportunity to work on large-scale, enterprise data platforms
• Exposure to modern cloud-native data engineering technologies
• Collaborative environment with strong stakeholder interaction
• Career growth and leadership opportunities

About the Role:

We are seeking a skilled Data Engineer to join our growing AdTech team. In this role, you will design, build, and maintain high-performance ETL pipelines and large-scale data processing systems. You will work with massive datasets and distributed frameworks to power Adsremedy’s data-driven advertising solutions across Programmatic, In-App, CTV, and DOOH platforms.

What You’ll Do:

• Design, develop, and maintain scalable ETL pipelines on self-managed infrastructure
• Process and optimize large-scale datasets (terabytes of data) with high reliability and performance
• Build robust data processing workflows using Apache Spark (preferred) and/or Apache Flink
• Integrate, clean, and transform data from multiple internal and external sources
• Partner closely with data scientists, analysts, and business stakeholders to enable actionable insights
• Monitor, troubleshoot, and optimize data pipelines for operational excellence
• Ensure data quality, consistency, and performance across all data workflows
• Participate in code reviews and uphold best practices in data engineering
• Collaborate with QA teams to deliver production-ready, reliable systems
• Mentor junior engineers and promote knowledge sharing within the team
• Stay current with emerging data engineering tools, frameworks, and industry trends

What You’ll Need:

• 2+ years of experience building ETL pipelines using Apache Spark and/or Apache Flink
• Hands-on experience with big data caching solutions such as ScyllaDB, Aerospike, or similar
• Strong understanding of data lake architectures and tools like Delta Lake
• Proven experience handling terabytes of data in distributed environments
• Proficiency in Scala, Python, or Java
• Experience working with cloud data platforms (AWS S3, Azure Data Lake, Google BigQuery)
• Strong knowledge of SQL, data modeling, and data warehousing concepts
• Familiarity with Git and CI/CD workflows
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment

Nice to Have

• Experience with Apache Kafka for real-time data streaming
• Familiarity with Apache Airflow or similar orchestration tools

• 10+ years of software development experience
• 3+ years in a technical leadership role
• Strong expertise in Python and SQL
• Experience building scalable APIs and backend systems
• Solid understanding of database design and performance tuning
• Experience with Azure cloud services (AWS familiarity preferred)
• Working knowledge of ML/AI integration in enterprise systems
• Experience in client-facing or consulting environments preferred
• Experience with Databricks or modern data platforms
• Exposure to ETL tools such as Talend
• Experience with BI tools (e.g., Power BI)
• Exposure to regulated domains such as Pharma, Healthcare

Job Title: Data Engineer

About the Role

We are looking for a highly motivated Data Engineer to join our growing team and play

a critical role in shaping the data foundation of different software platforms. This role sits

at the intersection of data engineering, product, and business stakeholders, and is

responsible for building reliable data pipelines, delivering actionable insights, and

ensuring data quality across systems.

You will work closely with internal teams and external partners to translate business

requirements into scalable data solutions, while maintaining high standards for data

integrity, performance, and usability.

Key Responsibilities

Data Engineering & Architecture

 Design, build, and maintain scalable data pipelines and ETL/ELT processes

 Develop and optimize data models in PostgreSQL and cloud-native

architectures

 Work within AWS ecosystem (e.g., S3, Lambda, RDS, Glue, Redshift, etc.) to

support data workflows

 Ensure efficient ingestion and processing of large-scale datasets

Business & Partner Integration

 Collaborate directly with business stakeholders and external partners to

gather requirements and deliver reporting solutions

 Translate ambiguous business needs into structured data models and

dashboards

 Integrate with third-party APIs and other external data sources

Data Quality & Governance

 Implement robust data validation, monitoring, and QA processes

 Ensure consistency, accuracy, and reliability of data across the platform

 Troubleshoot and resolve data discrepancies proactively

Reporting & Analytics Enablement

 Build datasets and pipelines that power dashboards and reporting tools

 Support internal teams with ad hoc analysis and data requests

 Partner with product and engineering teams to embed data into the SaaS product experience

Performance & Scalability

 Optimize queries, pipelines, and storage for performance and cost efficiency

 Continuously improve system scalability as data volume and complexity grow

Required Qualifications

 3–6+ years of experience in Data Engineering or related role

 Strong proficiency in Python for data processing and scripting

 Advanced experience with PostgreSQL (query optimization, schema design)

 Hands-on experience with AWS data architecture (S3, RDS, Lambda, Glue,

Redshift, etc.)

 Experience integrating with external APIs

 Solid understanding of ETL/ELT pipelines, data modeling, and warehousing

concepts

 Experience working cross-functionally with business stakeholders

Preferred Qualifications

 Experience in AdTech, eCommerce, or SaaS platforms

 Familiarity with BI tools (e.g., Looker, Tableau, Power BI)

 Experience with workflow orchestration tools (e.g., Airflow)

 Understanding of data governance and compliance best practices

 Exposure to real-time or streaming data pipelines

What We’re Looking For

 Strong problem-solver who can operate in a fast-paced, ambiguous

environment

 Ability to balance technical depth with business context

 Excellent communication skills — able to work directly with non-technical

stakeholders

 Ownership mindset with a focus on execution and quality

Role & Responsibilities:

We are looking for a strong Data Engineer to join our growing team. The ideal candidate brings solid ETL fundamentals, hands-on pipeline experience, and cloud platform proficiency — with a preference for GCP / BigQuery expertise.

Responsibilities:

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows
• Work with Dataform or DBT to implement transformation logic and data models
• Develop and optimize data solutions on GCP (BigQuery, GCS) or AWS/Azure
• Support data migration initiatives and data mesh architecture patterns
• Collaborate with analysts, scientists, and business stakeholders to deliver reliable data products
• Apply data governance and quality best practices across the data lifecycle
• Troubleshoot pipeline issues and drive proactive monitoring and resolution

Ideal Candidate:

• Strong Data Engineer Profile
• Must have 6+ years of hands-on experience in Data Engineering, with strong ownership of end-to-end data pipeline development.
• Must have strong experience in ETL/ELT pipeline design, transformation logic, and data workflow orchestration.
• Must have hands-on experience with any one of the following: Dataform, dbt, or BigQuery, with practical exposure to data transformation, modeling, or cloud data warehousing.
• Must have working experience on any cloud platform: GCP (preferred), AWS, or Azure, including object storage (GCS, S3, ADLS).
• Must have strong SQL skills with experience in writing complex queries and optimizing performance.
• Must have programming experience in Python and/or SQL for data processing.
• Must have experience in building and maintaining scalable data pipelines and troubleshooting data issues.
• Exposure to data migration projects and/or data mesh architecture concepts.
• Experience with Spark / PySpark or large-scale data processing frameworks.
• Experience working in product-based companies or data-driven environments.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

NOTE:

• There will be an interview drive scheduled on 28th and 29th March 2026, and if shortlisted, they will be expected to be available on these Interview dates. Only Immediate joiners are considered.

Note-“Urgently Hiring – Immediate Joiners Preferred”

Data Engineering

Role & Responsibilities

We are looking for a strong Data Engineer to join our growing team. The ideal candidate brings solid ETL fundamentals, hands-on pipeline experience, and cloud platform proficiency — with a preference for GCP/BigQuery expertise.

Responsibilities:

• Design, build, and maintain scalable data pipelines and ETL/ELT workflows
• Work with Dataform or dbt to implement transformation logic and data models
• Develop and optimize data solutions on GCP (BigQuery, GCS) or AWS/Azure
• Support data migration initiatives and data mesh architecture patterns
• Collaborate with analysts, scientists, and business stakeholders to deliver reliable data products
• Apply data governance and quality best practices across the data lifecycle
• Troubleshoot pipeline issues and drive proactive monitoring and resolution

Ideal Candidate

• Strong Data Engineer Profile
• Mandatory (Experience 1) – Must have 6+ years of hands-on experience in Data Engineering, with strong ownership of end-to-end data pipeline development.
• Mandatory (Experience 2) – Must have strong experience in ETL/ELT pipeline design, transformation logic, and data workflow orchestration.
• Mandatory (Experience 3) – Must have hands-on experience with any one of the following: Dataform, dbt, or BigQuery, with practical exposure to data transformation, modeling, or cloud data warehousing.
• Mandatory (Experience 4) – Must have working experience on any cloud platform: GCP (preferred), AWS, or Azure, including object storage (GCS, S3, ADLS).
• Mandatory (Core Skill 1) – Must have strong SQL skills with experience in writing complex queries and optimizing performance.
• Mandatory (Core Skill 2) – Must have programming experience in Python and/or SQL for data processing.
• Mandatory (Core Skill 3) – Must have experience in building and maintaining scalable data pipelines and troubleshooting data issues.
• Preferred (Experience 1) – Exposure to data migration projects and/or data mesh architecture concepts.
• Preferred (Skill 1) – Experience with Spark/PySpark or large-scale data processing frameworks.
• Preferred (Company) – Experience working in product-based companies or data-driven environments.
• Preferred (Education) – Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

.

The Mission: We are looking for a visionary Technical Leader to own our healthcare data ecosystem from the first byte to the final dashboard. You won't just be managing a platform; you’ll be the primary architect of a clinical data engine that powers life-changing analytics. If you are an expert in SQL and Python who thrives on solving the "puzzle" of healthcare interoperability (FHIR/HL7) while mentoring a high-performing team, this is your seat at the table.

What You’ll Own

• Architectural Sovereignty: Define the end-to-end blueprint for our data warehouse (staging, marts, and semantic layers). You choose the frameworks, set the coding standards, and decide how we handle complex dimensional modeling and SCDs.
• Engineering Excellence: Lead by example. You’ll write production-grade Python for ingestion frameworks and craft advanced, set-based SQL transformations that others use as gold-standard references.
• The Interoperability Bridge: Turn the chaos of EHR exports, REST APIs, and claims data into clean, FHIR-aligned governed datasets. You ensure our data speaks the language of modern healthcare.
• Technical Mentorship: Act as the "Engineer’s Engineer." You’ll run design reviews, champion CI/CD best practices, and build the runbooks that keep our small but mighty team efficient.
• Security by Design: Direct the implementation of HIPAA-compliant data flows, ensuring encryption, auditability, and access controls are baked into the architecture, not bolted on.

The Stack You’ll Command

• Languages: Expert-level SQL (CTE, Window Functions, Tuning) and Production Python.
• Databases: Deep polyglot experience across MSSQL, PostgreSQL, Oracle, and NoSQL (MongoDB/Elasticsearch).
• Orchestration: Advanced Apache Airflow (SLAs, retries, and complex DAGs).
• Ecosystem: GitHub for CI/CD, Tableau/PowerBI for semantic layers, and Unix/Linux for shell scripting.

Who You Are

• Experienced: You have 8–12+ years in data engineering, with a significant portion spent in a Lead or Architect capacity.
• Healthcare-Fluent: You understand the stakes of PHI. You’ve worked with FHIR/HL7 and know how to map clinical resources to analytical models.
• Performance-Obsessed: You don’t just make it work; you make it fast. You’re the person who uses EXPLAIN/ANALYZE to shave minutes off a query.
• Culture-Builder: You believe in documentation, observability (lineage/freshness), and "leaving the campground cleaner than you found it."

Bonus Points for:

• Privacy Pro: Experience with PII/PHI de-identification and privacy-by-design.
• Cloud Native: Deep familiarity with Azure, AWS, or GCP security and data services.
• Search Experts: Experience with near-real-time indexing via Elasticsearch.

To process your resume for the next process, please fill out the Google form with your updated resume.

Pre-screen Question: https://forms.gle/q3CzfdSiWoXTCEZJ7

Details: https://forms.gle/FGgkmQvLnS8tJqo5A

Job Description & Specification: 

Post Title: Data Engineer

Work Mode: Kochi Onsite - UK Time zone

Role Overview: 

We are seeking a talented and experienced Data Engineer to join our team. The ideal candidate will have expertise in technologies such as Metabases, Dbt, Stitch, Snowflake, Avo, and MongoDB. As a Data Engineer, you will play a crucial role in designing, developing, and maintaining our data infrastructure to support our analytics and data-driven decision-making processes.

Responsibilities:

• Designing, developing and implementing scalable data pipelines and ETL processes using tools such as Stitch and Dbt to ingest, transform, and load data from various sources into our data warehouse (Snowflake).
• Implement data modeling best practices and standards using Dbt to create and manage data models for reporting and analytics.
• Collaborating with cross-functional teams to understand data requirements and deliver solutions that meet business needs.
• Develop and maintain dashboards and visualizations in Metabases to enable self-service analytics and data exploration for internal teams.
• Building and optimizing ETL processes to ensure data quality and integrity.
• Optimizing data processing and storage solutions for performance, scalability and reliability, leveraging cloud-based technologies.
• Implementing monitoring and alerting systems to proactively identify and address data issues.
• Implementing data quality checks and monitoring processes to ensure the accuracy, completeness, and integrity of data.
• Managing and optimizing databases (like MongoDB for performance and scalability).
• Developing and maintaining documentation, best practices, and standards for data engineering processes and workflows.
• Stay up to date with emerging technologies and trends in data engineering, machine learning, and analytics, and evaluate their potential impact on data strategy and architecture.

Requirements:

• Bachelor's or Master's degree in Computer Science.
• Minimum of 4 years of experience working as a data engineer with expertise in Metabases, Dbt, Stitch, Snowflake, Avo, MongoDB.
• Strong programming skills in languages like Python, and experience with SQL and database technologies (e.g., PostgreSQL, MySQL, MongoDB).
• Hands-on experience with data integration tools (e.g., Stitch), data modeling tools (e.g., Dbt), and BI platforms (e.g., Metabases).
• Experience with cloud platforms such as AWS.
• Strong understanding of data modeling concepts, database design, and data warehousing principles
• Experience with big data technologies and frameworks (e.g., Hadoop, Spark, Kafka) and cloud-based data platforms (e.g., AWS EMR, Azure Databricks, Google BigQuery).
• Familiarity with data integration tools, ETL processes, and workflow orchestration tools (e.g., Apache Airflow, Apache NiFi).
• Excellent problem-solving skills and attention to detail.
• Strong communication skills with the ability to work effectively in a global team environment.
• Experience in the education or Edtech industry is a plus.
• Knowledge of Avo for schema management and versioning will be an added advantage.
• Familiarity with machine learning algorithms, data science workflows, and analytics tools (e.g., TensorFlow, PyTorch, scikit-learn, Tableau).
• Knowledge of distributed computing concepts and containerization technologies.
• Experience with version control systems (e.g., Git) and CI/CD pipelines.
• Certifications in cloud computing (e.g., AWS Certified Developer, Google Cloud Professional Data Engineer) or data engineering (e.g., Databricks Certified Associate Developer) are desirable.

Benefits:

• Competitive salary and bonus structure based on performance and achievement of goals.
• Comprehensive benefits package including medical insurance.

Join us in shaping the future of technology by applying your expertise as a Data Engineer. If you are passionate about driving innovation and delivering impactful solutions, we invite you to be part of our dynamic team. Apply now!!

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

• Legacy systems → AI-native architectures
• Static pipelines → autonomous, self-healing systems
• Data platforms → intelligent, learning systems
• Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

• Data platforms optimize themselves using ML/LLMs
• Pipelines are autonomous, self-healing, and adaptive
• Queries are generated, optimized, and executed intelligently
• Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

• Build LLM-powered interfaces:
• Natural language → SQL / pipelines / transformations
• Design semantic data layers:
• Embeddings, vector search, knowledge graphs
• Develop AI copilots:
• For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

• Build self-healing ETL/ELT systems using AI agents
• Create pipelines that:
• Detect anomalies in real time
• Automatically debug failures
• Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

• Apply ML/LLMs to:
• Query planning and execution
• Cost-based optimization using learned models
• Workload prediction and scheduling
• Build systems that:
• Learn from query patterns
• Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

• Architect systems operating at:
• Billions of events per day
• Petabyte-scale data
• Work with:
• Distributed compute engines (Spark / Flink / Ray class systems)
• Streaming systems (Kafka-class infra)
• Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

• Build closed-loop AI systems:
• Execution → feedback → model updates
• Develop:
• Continual learning pipelines
• Online learning systems for infra optimization
• Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

• Build agents that understand data systems
• Enable:
• Autonomous pipeline debugging
• Root cause analysis for infra failures
• Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

• Strong grounding in:
• Machine Learning, Deep Learning, NLP
• Statistics, optimization, probabilistic systems
• Distributed systems fundamentals
• Deep understanding of:
• Transformer architectures
• Modern LLM ecosystems

Hands-On Expertise

• Experience building:
• LLM / GenAI systems (RAG, fine-tuning, embeddings)
• Data platforms (warehouse, lake, lakehouse architectures)
• Distributed pipelines and compute systems
• Strong programming skills:
• Python (ML/AI stack)
• SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

• Built or worked on:
• Large-scale data pipelines
• High-throughput distributed systems
• Low-latency, high-concurrency architectures
• Understand:
• Query optimization and execution
• Data partitioning, indexing, caching
• Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

• Built AI-powered data platforms or infra systems in production
• Designed or contributed to:
• Query engines / optimizers
• Data observability / lineage systems
• AI-driven infra or AIOps platforms
• Experience with:
• Multi-modal AI (logs, metrics, traces, text)
• Agentic AI systems
• Autonomous infrastructure
• Worked on systems at scale comparable to:
• Google (BigQuery-like systems)
• Meta (real-time analytics infra)
• Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

• Data infrastructure or platform engineering teams
• AI-first startups or research-driven environments
• High-scale product companies

Experience building:

• Internal platforms used by 1000s of engineers
• Systems serving millions of users / high throughput workloads
• Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

• Can LLMs replace traditional query optimizers?
• How do we build self-healing data pipelines at scale?
• Can data systems learn from every query and improve automatically?
• How do we embed reasoning and planning into infrastructure layers?
• What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

• Leading engineering schools in India and globally
• Engineers with experience in top product companies, AI startups, or research-driven environments
• That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

Job Details

- Job Title: Senior Backend Engineer

- Industry: SAAS

- Function – Information Technology

- Experience Required: 5-8 years

- Working Days: 6 days a week, (5 days-in-office, Saturdays WFH)

- Employment Type: Full Time

- Job Location: Bangalore

- CTC Range: Best in Industry

Preferred Skills: AWS, NodeJS, RESTful APIs, NoSQL

Criteria

· Minimum 5+ years in backend engineering with strong system design expertise

· Experience building scalable systems from scratch

· Expert-level proficiency in Node.js

· Deep understanding of distributed systems

· Strong NoSQL design skills

· Hands-on AWS cloud experience

· Proven leadership and mentoring capability

· Preferred candidates from SAAS/Software/IT Services based startups or scaleup companies

Job Description

The Role:

What You’ll Build:

1. System Architecture & Design

● Architect highly scalable backend systems from the ground up

● Define technology choices: frameworks, databases, queues, caching layers

● Evaluate microservices vs monoliths based on product stage

● Design REST, GraphQL, and real-time WebSocket APIs

● Build event-driven systems for asynchronous processing

● Architect multi-tenant systems with strict data isolation

● Maintain architectural documentation and technical specs

2. Core Backend Services

● Build high-performance APIs for 3D content, XR experiences, analytics, and user interactions

● Create 3D asset processing pipelines for uploads, conversions, and optimization

● Develop distributed job workers for CPU/GPU-intensive tasks

● Build authentication/authorization systems (RBAC)

● Implement billing, subscription, and usage metering

● Build secure webhook systems and third-party integration APIs

● Create real-time collaboration features via WebSockets/SSE

3. Data Architecture & Databases

● Design scalable schemas for 3D metadata, XR sessions, and analytics

● Model complex product catalogs with variants and hierarchies

● Implement Redis-based caching strategies

● Build search and indexing systems (Elasticsearch/Algolia)

● Architect ETL pipelines and data warehouses

● Implement sharding, partitioning, and replication strategies

● Design backup, restore, and disaster recovery workflows

4. Scalability & Performance

● Build systems designed for 10x–100x traffic growth

● Implement load balancing, autoscaling, and distributed processing

● Optimize API response times and database performance

● Implement global CDN delivery for heavy 3D assets

● Build rate limiting, throttling, and backpressure mechanisms

● Optimize storage and retrieval of large 3D files

● Profile and improve CPU, memory, and network performance

5. Infrastructure & DevOps

● Architect AWS infrastructure (EC2, S3, Lambda, RDS, ElastiCache)

● Build CI/CD pipelines for automated deployments and rollbacks

● Use IaC tools (Terraform/CloudFormation) for infra provisioning

● Set up monitoring, logging, and alerting systems

● Use Docker + Kubernetes for container orchestration

● Implement security best practices for data, networks, and secrets

● Define disaster recovery and business continuity plans

6. Integration & APIs

● Build integrations with Shopify, WooCommerce, Magento

● Design webhook systems for real-time events

● Build SDKs, client libraries, and developer tools

● Integrate payment gateways (Stripe, Razorpay)

● Implement SSO and OAuth for enterprise customers

● Define API versioning and lifecycle/deprecation strategies

7. Data Processing & Analytics

● Build analytics pipelines for engagement, conversions, and XR performance

● Process high-volume event streams at scale

● Build data warehouses for BI and reporting

● Develop real-time dashboards and insights systems

● Implement analytics export pipelines and platform integrations

● Enable A/B testing and experimentation frameworks

● Build personalization and recommendation systems

Technical Stack:

1. Backend Languages & Frameworks 

●  Primary: Node.js (Express, NestJS), Python (FastAPI, Django)

●  Secondary: Go, Java/Kotlin (Spring)

●  APIs: REST, GraphQL, gRPC

2. Databases & Storage

● SQL: PostgreSQL, MySQL

● NoSQL: MongoDB, DynamoDB

● Caching: Redis, Memcached

● Search: Elasticsearch, Algolia

● Storage/CDN: AWS S3, CloudFront

● Queues: Kafka, RabbitMQ, AWS SQS

3. Cloud & Infrastructure: 

● Cloud: AWS (primary), GCP/Azure (nice to have)

● Compute: EC2, Lambda, ECS, EKS

● Infrastructure: Terraform, CloudFormation

● CI/CD: GitHub Actions, Jenkins, CircleCI

● Containers: Docker, Kubernetes

4. Monitoring & Operations 

● Monitoring: Datadog, New Relic, CloudWatch

● Logging: ELK Stack, CloudWatch Logs

● Error Tracking: Sentry, Rollbar

● APM tools

5. Security & Auth

● Auth: JWT, OAuth 2.0, SAML

● Secrets: AWS Secrets Manager, Vault

● Security: Encryption (at rest/in transit), TLS/SSL, IAM

What We’re Looking For:

1. Must-Haves

● 5+ years in backend engineering with strong system design expertise

● Experience building scalable systems from scratch

● Expert-level proficiency in at least one backend stack (Node, Python, Go, Java)

● Deep understanding of distributed systems and microservices

● Strong SQL/NoSQL design skills with performance optimization

● Hands-on AWS cloud experience

● Ability to write high-quality production code daily

● Experience building and scaling RESTful APIs

● Strong understanding of caching, sharding, horizontal scaling

● Solid security and best-practice implementation experience

● Proven leadership and mentoring capability

2. Highly Desirable

● Experience with large file processing (3D, video, images)

● Background in SaaS, multi-tenancy, or e-commerce

● Experience with real-time systems (WebSockets, streams)

● Knowledge of ML/AI infrastructure

● Experience with HA systems, DR planning

● Familiarity with GraphQL, gRPC, event-driven systems

● DevOps/infrastructure engineering background

● Experience with XR/AR/VR backend systems

● Open-source contributions or technical writing

● Prior senior technical leadership experience

Technical Challenges You’ll Solve:

● Designing large-scale 3D asset processing pipelines

● Serving XR content globally with ultra-low latency

● Scaling from thousands to millions of daily requests

● Efficiently handling CPU/GPU-heavy workloads

● Architecting multi-tenancy with complete data isolation

● Managing billions of analytics events at scale

● Building future-proof APIs with backward compatibility

Why company:

● Architectural Ownership: Build foundational systems from scratch

● Deep Technical Work: Solve distributed systems and scaling challenges

● Hands-On Impact: Design and code mission-critical infrastructure

● Diverse Problems: APIs, infra, data, ML, XR, asset processing

● Massive Scale Opportunity: Build systems for exponential growth

● Modern Stack and best practices

● Product Impact: Your architecture directly powers millions of users

● Leadership Opportunity: Shape engineering culture and direction

● Learning Environment: Stay at the forefront of backend engineering

● Backed by AWS, Microsoft, Google

Location & Work Culture:

● Location: Bengaluru

● Schedule: 6 days a week, (5 days-in-office, Saturdays WFH)

● Culture: Builder mindset, strong ownership, technical excellence

● Team: Small, highly skilled backend and infra team

● Resources: AWS credits, latest tooling, learning budget

Description

Power BI JD

Mandatory:

• 5+ years of Power BI Report development experience.

• Building Analysis Services reporting models.

• Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.

• Connecting data sources, importing data, and transforming data for Business intelligence.

• Analytical thinking for translating data into informative reports and visuals.

• Capable of implementing row-level security on data along with an understanding of application security layer models in Power BI.

• Should have an edge over making DAX queries in Power BI desktop.

• Expert in using advanced-level calculations on the data set.

• Responsible for design methodology and project documentaries.

• Should be able to develop tabular and multidimensional models that are compatible with data warehouse standards.

• Very good communication skills must be able to discuss the requirements effectively with the client teams, and with internal teams.

• Experience working with Microsoft Business Intelligence Stack having Power BI, SSAS, SSRS, and SSIS

• Mandate to have experience with BI tools and systems such as Power BI, Tableau, and SAP.

• Must have 3-4years of experience in data-specific roles.

• Have knowledge of database fundamentals such as multidimensional database design, relational database design, and more

• Knowledge of all the Power BI products (Power Bi premium, Power BI server, Power BI services, Powerquery etc)

• Grip over data analytics

• Interact with customers to understand their business problems and provide best-in-class analytics solutions

• Proficient in SQL and Query performance tuning skills

• Understand data governance, quality and security and integrate analytics with these corporate platforms

• Attention to detail and ability to deliver accurate client outputs

• Experience of working with large and multiple datasets / data warehouses

• Ability to derive insights from data and analysis and create presentations for client teams

• Experience with performance optimization of the dashboards

• Interact with UX/UI designers to create best in class visualization for business harnessing all product capabilities.

• Resilience under pressure and against deadlines.

• Proactive attitude and an open outlook.

• Strong analytical problem-solving skills

• Skill in identifying data issues and anomalies during the analysis

• Strong business acumen demonstrated an aptitude for analytics that incite action

• Ability to execute on design requirements defined by business

• Ability to understand required Power BI functionality from wireframes/ requirement documents

• Ability to architect and design reporting solutions based on client needs.

• Being able to communicate with internal/external customers, desire to develop communication and client-facing skills.

• Ability to seamlessly work with MS Excel working knowledge of pivot table and related functions

Good to have:

• Experience in working with Azure and connecting synapse with Tableau

• Demonstrate strength in data modelling, ETL development, and data warehousing

• Knowledge of leading large-scale data warehousing and analytics projects using Azure, Synapse, MS SQL DB

• Good knowledge of building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets

• Good to have knowledge of Supply Chain Domain.

• Design, develop, and deploy interactive Power BI dashboards and reports for client projects
• Build and optimize data models using star schema and snowflake schema design patterns
• Develop complex DAX measures and calculated columns to support business requirements
• Connect to and integrate data from multiple sources including SQL databases, Excel, APIs, cloud platforms, and data warehouses
• Implement data transformation and cleansing using Power Query (M language)
• Collaborate with clients and stakeholders to gather requirements and translate business needs into technical specifications
• Optimize report performance through query optimization, aggregations, and efficient data modeling
• Configure and manage Power BI Service including workspaces, datasets, dataflows, and row-level security (RLS)
• Create and maintain documentation for data models, reports, and development processes
• Provide training and support to end-users on Power BI reports and dashboards
• Design and implement custom visualizations using Power BI custom visuals and third-party visual libraries.
• Implement conditional formatting, dynamic titles, and dynamic content based on user selections
• Stay current with Power BI updates, new features, and industry best practices

Blue Owls Solutions is looking for a mid-level Azure Data Engineer with approximately 4 years of hands-on experience to join our growing data team. In this role, you will design, build, and maintain scalable data pipelines and architectures that power business-critical analytics and reporting. You'll work closely with cross-functional teams to transform raw data into reliable, high-quality datasets that drive decision-making across the organization.

Required Skills

• 4+ years of professional experience as a Data Engineer or in a similar data-focused role
• Strong proficiency in SQL for data manipulation, querying, and performance optimization
• Hands-on experience with PySpark for large-scale data processing and transformation
• Solid working knowledge of the Microsoft Azure ecosystem (Azure Data Factory, Azure Data Lake, Azure Synapse, etc.)
• Experience with Microsoft Fabric for end-to-end data analytics workflows
• Ability to design and implement robust data architectures including data warehouses, lakehouses, and ETL/ELT frameworks
• Strong coding and scripting skills with Python
• Proven problem-solving ability with a knack for debugging complex data issues and optimizing pipeline performance
• Understanding of data modeling concepts, dimensional modeling, and data governance best practices

Interview Process

• Take-Home Assessment
• 60-Minute Technical Interview
• Culture Fit Round

Preferred Skills & Certifications

• Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)
• Microsoft Certified: Fabric Data Engineer Associate (DP-700)
• Experience with CI/CD practices for data pipelines
• Familiarity with version control systems such as Git
• Exposure to real-time streaming data solutions
• Experience working in Agile or Scrum environments
• Strong communication skills with the ability to translate technical concepts for non-technical stakeholders

What We Offer

• Competitive salary and performance-based bonuses
• Flexible hybrid options
• Opportunities for professional development, training, and certification sponsorship
• A collaborative, innovation-driven team culture
• Paid time off and company holidays

Position Title : Senior Data Engineer(Founding Member) - Insurtech StartUp

Location : Hyderabad(Onsite)

Immediate to 15 days Joiners

Experience : 5+ to 13 Years

Role Summary

We are looking for a Senior Data Engineer who will play a foundational role in:

• Client onboarding from a data perspective
• Understanding complex insurance data flows
• Designing secure, scalable ingestion pipelines
• Establishing strong data modeling and governance standards

This role sits at the intersection of technology, data architecture, security, and business onboarding.

.

Key Responsibilities

• Lead end-to-end data onboarding for new clients and partners, working closely with business and product teams to understand client systems, data formats, and migration constraints
• Define and implement data ingestion strategies supporting multiple sources and formats, including CSV, XML, JSON files, and API-based integrations
• Design, build, and operate robust, scalable ETL/ELT pipelines, supporting both batch and near-real-time data processing
• Handle complex insurance-domain data including Contracts, Claims, Reserves, Cancellations, and Refunds
• Architect ingestion pipelines with security-by-design principles, including secure credential management (keys, secrets, tokens), encryption at rest and in transit, and network-level controls where required
• Enforce role-based and attribute-based access controls, ensuring strict data isolation, tenancy boundaries, and stakeholder-specific access rules
• Design, maintain, and evolve canonical data models that support operational workflows, reporting & analytics, and regulatory/audit requirements
• Define and enforce data governance standards, ensuring compliance with insurance and financial data regulations and consistent definitions of business metrics across stakeholders
• Build and operate data pipelines on a cloud-native platform, leveraging distributed processing frameworks (Spark / PySpark), data lakes, lakehouses, and warehouses
• Implement and manage orchestration, monitoring, alerting, and cost-optimization mechanisms across the data platform
• Contribute to long-term data strategy, platform architecture decisions, and cost-optimization initiatives while maintaining strict security and compliance standards

Required Technical Skills

• Core Stack: Python, Advanced SQL(Complex joins, window functions, performance tuning), Pyspark
• Platforms: Azure, AWS, Data Bricks, Snowflake
• ETL / Orchestration: Airflow or similar frameworks
• Data Modeling: Star/Snowflake schema, dimensional modeling, OLAP/OLTP
• Visualization Exposure: Power BI
• Version Control & CI/CD: GitHub, Azure Devops, or equivalent
• Integrations: APIs, real-time data streaming, ML model integration exposure

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 5+ years of experience in data engineering or similar roles
• Strong ability to align technical solutions with business objectives
• Excellent communication and stakeholder management skills

What We Offer

• Direct collaboration with the core US data leadership team
• High ownership and trust to manage the function end-to-end
• Exposure to a global environment with advanced tools and best practices

Job Description:

Position Type: Full-Time Contract (with potential to convert to Permanent)

Location: Remote (Australian Time Zone)

Availability: Immediate Joiners Preferred

About the Role

We are seeking an experienced Tableau and Snowflake Specialist with 5+ years of hands‑on expertise to join our team as a full‑time contractor for the next few months. Based on performance and business requirements, this role has a strong potential to transition into a permanent position.

The ideal candidate is highly proficient in designing scalable dashboards, managing Snowflake data warehousing environments, and collaborating with cross-functional teams to drive data‑driven insights.

Key Responsibilities

• Develop, design, and optimize advanced Tableau dashboards, reports, and visual analytics.
• Build, maintain, and optimize datasets and data models in Snowflake Cloud Data Warehouse.
• Collaborate with business stakeholders to gather requirements and translate them into analytics solutions.
• Write efficient SQL queries, stored procedures, and data pipelines to support reporting needs.
• Perform data profiling, data validation, and ensure data quality across systems.
• Work closely with data engineering teams to improve data structures for better reporting efficiency.
• Troubleshoot performance issues and implement best practices for both Snowflake and Tableau.
• Support deployment, version control, and documentation of BI solutions.
• Ensure availability of dashboards during Australian business hours.

Required Skills & Experience

• 5+ years of strong hands-on experience with Tableau development (Dashboards, Storyboards, Calculated Fields, LOD Expressions).
• 5+ years of experience working with Snowflake including schema design, warehouse configuration, and query optimization.
• Advanced knowledge of SQL and performance tuning.
• Strong understanding of data modeling, ETL processes, and cloud data platforms.
• Experience working in fast-paced environments with tight delivery timelines.
• Excellent communication and stakeholder management skills.
• Ability to work independently and deliver high‑quality outputs aligned with business objectives.

Nice-to-Have Skills

• Knowledge of Python or any ETL tool.
• Experience with Snowflake integrations (Fivetran, DBT, Azure/AWS/GCP).
• Tableau Server/Prep experience.

Contract Details

• Full-Time Contract for several months.
• High possibility of conversion to permanent, based on performance.
• Must be available to work on the Australian Time Zone.
• Immediate joiners are highly encouraged.

Job Title: Data Engineer

Experience: 4–14 Years

Work Mode: Remote

Employment Type: Full-Time

Position Overview:

We are looking for highly experienced Senior Data Engineers to design, architect, and lead scalable, cloud-based data platforms on AWS. The role involves building enterprise-grade data pipelines, modernizing legacy systems, and developing high-performance scoring engines and analytics solutions and collaborate closely with architecture, analytics, risk, and business teams to deliver secure, reliable, and scalable data solutions.

Key Responsibilities:

·      Design and build scalable data pipelines for financial and customer data

·      Build and optimize scoring engines (credit, risk, fraud, customer scoring)

·      Design, develop, and optimize complex ETL/ELT pipelines (batch & real-time)

·      Ensure data quality, governance, reliability, and compliance standards

·      Optimize large-scale data processing using SQL, Spark/PySpark, and cloud technologies

·      Lead cloud data architecture, cost optimization, and performance tuning initiatives

·      Collaborate with Data Science, Analytics, and Product teams to deliver business-ready datasets

·      Mentor junior engineers and establish best practices for data engineering

Key Requirements:

·      Strong programming skills in Python and advanced SQL

·      Experience building scalable scoring or rule-based decision engines

·      Hands-on experience with Big Data technologies (Spark/PySpark/Kafka)

·      Strong expertise in designing ETL/ELT pipelines and data modeling

·      Experience with cloud platforms (AWS/Azure) and modern data architectures

·      Solid understanding of data warehousing, data lakes, and performance tuning

·      Knowledge of CI/CD, version control (Git), and production support best practices

Description

We are currently hiring for the position of Data Scientist/ Senior Machine Learning Engineer (6–7 years’ experience).

Please find the detailed Job Description attached for your reference. We are looking for candidates with strong experience in:

• Machine Learning model development
• Scalable data pipeline development (ETL/ELT)
• Python and SQL
• Cloud platforms such as Azure/AWS/Databricks
• ML deployment environments (SageMaker, Azure ML, etc.)

Kindly note:

• Location: Pune (Work From Office)
• Immediate joiners preferred

While sharing profiles, please ensure the following details are included:

• Current CTC
• Expected CTC
• Notice Period
• Current Location
• Confirmation on Pune WFO comfort

Must have skills

Machine Learning - 6 years

Python - 6 years

ETL(Extract, Transform, Load) - 6 years

SQL - 6 years

Azure - 6 years

Data Scientist or Senior Machine Learning Engineer

We are currently hiring for the position of Data Scientist/ Senior Machine Learning Engineer (6–7 years' experience).

Please find the detailed Job Description attached for your reference.

We are looking for candidates with strong experience in:

• Machine Learning model development
• Scalable data pipeline development (ETL/ELT)
• Python and SQL
• Cloud platforms such as Azure/AWS/Databricks
• ML deployment environments (SageMaker, Azure ML, etc.)

Kindly note:

• Location: Pune (Work from Office)
• Immediate joiners preferred

While sharing profiles, please ensure the following details are included:

• Current CTC
• Expected CTC
• Notice Period
• Current Location
• Confirmation on Pune WFO comfort

Must have Skills

• Machine Learning - 6 Years
• Python - 6 Years
• ETL (Extract, Transform, Load) - 6 Years
• SQL - 6 Years
• Azure - 6 Years

Request you to share relevant profiles at the earliest. Looking forward to your support.

Salesforce Developer

Location : ONSITE

LOCATION : MUMBAI AND BANGALORE

Resources should have banking domain experience.

1. Salesforce development Engineer (1 - 3 Years) 

2. Salesforce development Engineer (3 - 5 Years) 

3. Salesforce development Engineer (5 - 8 Years) 

Job description. 

----------------------------------------------------------------------------

Technical Skills:

Strong hands-on frontend development using JavaScript and LWC

Expertise in backend development using Apex, Flows, Async Apex

Understanding of Database concepts: SOQL, SOSL and SQL

Hands-on experience in API integration using SOAP, REST API, graphql

Experience with ETL tools , Data migration, and Data governance

Experience with Apex Design Patterns, Integration Patterns and Apex testing framework

Follow agile, iterative execution model using CI-CD tools like Azure Devops, gitlab, bitbucket 

Should have worked with at least one programming language - Java, python, c++ and have good understanding of data structures

Preferred qualifications

Graduate degree in engineering

Experience developing with India stack

Experience in fintech or banking domain

----------------------------------------------------------------------------

 Skill details. 

1. Salesforce Fundamentals

Strong understanding of Salesforce core architecture

Objects (Standard vs Custom)

Fields, relationships (Lookup, Master-Detail)

Data model basics and record lifecycle

Awareness of declarative vs programmatic capabilities and when to use each

2. Salesforce Security Model

End-to-end understanding of Salesforce security layers, especially:

Record visibility when a record is created

Org-Wide Defaults (OWD) and their impact

Role Hierarchy and how it enables upward data access

Difference between Profiles, Permission Sets, and Sharing Rules

Ability to explain how Salesforce ensures that records are not visible to unauthorized users by default and how access is extended

3. Apex Triggers

Clear distinction between:

Before Triggers (before insert, before update)

Use cases such as validation and field updates

After Triggers (after insert, after update)

Use cases such as related record updates or integrations

Understanding of trigger context variables and best practices (bulkification, avoiding recursion)

4. Platform Events / Event-Driven Architecture

Knowledge of Platform Events and their use in decoupled, event-driven solutions

Understanding of real-time or near real-time notification use cases (e.g., UI alerts, pop-up style notifications)

Ability to position Platform Events versus alternatives (Streaming API, Change Data Capture)

5. Lightning Data Access (Wire Method)

Understanding of the @wire mechanism in Lightning Web Components (LWC)

Discussion point:

Whether records (e.g., AppX records) can be updated using the wire method

Awareness that @wire is primarily read/reactive and updates typically require imperative Apex calls

Clear articulation of reactive vs imperative data handling

6. Integrations Experience

Ability to articulate hands-on integration experience, including:

REST/SOAP API integrations

Inbound vs outbound integrations

Authentication mechanisms (OAuth, Named Credentials)

Use of Apex callouts, Platform Events, or middleware

Clarity on integration patterns and error handling approaches

JOB DETAILS:

* Job Title: Lead II - Software Engineering - AWS, Apache Spark (PySpark/Scala), Apache Kafka

* Industry: Global digital transformation solutions provider

* Salary: Best in Industry

* Experience: 5-8 years

* Location: Hyderabad

Job Summary

We are seeking a skilled Data Engineer to design, build, and optimize scalable data pipelines and cloud-based data platforms. The role involves working with large-scale batch and real-time data processing systems, collaborating with cross-functional teams, and ensuring data reliability, security, and performance across the data lifecycle.

Key Responsibilities

ETL Pipeline Development & Optimization

• Design, develop, and maintain complex end-to-end ETL pipelines for large-scale data ingestion and processing.
• Optimize data pipelines for performance, scalability, fault tolerance, and reliability.

Big Data Processing

• Develop and optimize batch and real-time data processing solutions using Apache Spark (PySpark/Scala) and Apache Kafka.
• Ensure fault-tolerant, scalable, and high-performance data processing systems.

Cloud Infrastructure Development

• Build and manage scalable, cloud-native data infrastructure on AWS.
• Design resilient and cost-efficient data pipelines adaptable to varying data volume and formats.

Real-Time & Batch Data Integration

• Enable seamless ingestion and processing of real-time streaming and batch data sources (e.g., AWS MSK).
• Ensure consistency, data quality, and a unified view across multiple data sources and formats.

Data Analysis & Insights

• Partner with business teams and data scientists to understand data requirements.
• Perform in-depth data analysis to identify trends, patterns, and anomalies.
• Deliver high-quality datasets and present actionable insights to stakeholders.

CI/CD & Automation

• Implement and maintain CI/CD pipelines using Jenkins or similar tools.
• Automate testing, deployment, and monitoring to ensure smooth production releases.

Data Security & Compliance

• Collaborate with security teams to ensure compliance with organizational and regulatory standards (e.g., GDPR, HIPAA).
• Implement data governance practices ensuring data integrity, security, and traceability.

Troubleshooting & Performance Tuning

• Identify and resolve performance bottlenecks in data pipelines.
• Apply best practices for monitoring, tuning, and optimizing data ingestion and storage.

Collaboration & Cross-Functional Work

• Work closely with engineers, data scientists, product managers, and business stakeholders.
• Participate in agile ceremonies, sprint planning, and architectural discussions.

Skills & Qualifications

Mandatory (Must-Have) Skills

1. AWS Expertise

• Hands-on experience with AWS Big Data services such as EMR, Managed Apache Airflow, Glue, S3, DMS, MSK, and EC2.
• Strong understanding of cloud-native data architectures.

1. Big Data Technologies

• Proficiency in PySpark or Scala Spark and SQL for large-scale data transformation and analysis.
• Experience with Apache Spark and Apache Kafka in production environments.

1. Data Frameworks

• Strong knowledge of Spark DataFrames and Datasets.

1. ETL Pipeline Development

• Proven experience in building scalable and reliable ETL pipelines for both batch and real-time data processing.

1. Database Modeling & Data Warehousing

• Expertise in designing scalable data models for OLAP and OLTP systems.

1. Data Analysis & Insights

• Ability to perform complex data analysis and extract actionable business insights.
• Strong analytical and problem-solving skills with a data-driven mindset.

1. CI/CD & Automation

• Basic to intermediate experience with CI/CD pipelines using Jenkins or similar tools.
• Familiarity with automated testing and deployment workflows.

Good-to-Have (Preferred) Skills

• Knowledge of Java for data processing applications.
• Experience with NoSQL databases (e.g., DynamoDB, Cassandra, MongoDB).
• Familiarity with data governance frameworks and compliance tooling.
• Experience with monitoring and observability tools such as AWS CloudWatch, Splunk, or Dynatrace.
• Exposure to cost optimization strategies for large-scale cloud data platforms.

Skills: big data, scala spark, apache spark, ETL pipeline development

******

Notice period - 0 to 15 days only

Job stability is mandatory

Location: Hyderabad

Note: If a candidate is a short joiner, based in Hyderabad, and fits within the approved budget, we will proceed with an offer

F2F Interview: 14th Feb 2026

3 days in office, Hybrid model.

JOB DETAILS:

* Job Title: Associate III - Data Engineering

* Industry: Global digital transformation solutions provide

* Salary: Best in Industry

* Experience: 4-6 years

* Location: Trivandrum, Kochi

Job Description

Job Title:

Data Services Engineer – AWS & Snowflake

Job Summary:

As a Data Services Engineer, you will be responsible for designing, developing, and maintaining robust data solutions using AWS cloud services and Snowflake.

You will work closely with cross-functional teams to ensure data is accessible, secure, and optimized for performance.

Your role will involve implementing scalable data pipelines, managing data integration, and supporting analytics initiatives.

Responsibilities:

• Design and implement scalable and secure data pipelines on AWS and Snowflake (Star/Snowflake schema)

• Optimize query performance using clustering keys, materialized views, and caching

• Develop and maintain Snowflake data warehouses and data marts.

• Build and maintain ETL/ELT workflows using Snowflake-native features (Snowpipe, Streams, Tasks).

• Integrate Snowflake with cloud platforms (AWS, Azure, GCP) and third-party tools (Airflow, dbt, Informatica)

• Utilize Snowpark and Python/Java for complex transformations

• Implement RBAC, data masking, and row-level security.

• Optimize data storage and retrieval for performance and cost-efficiency.

• Collaborate with stakeholders to gather data requirements and deliver solutions.

• Ensure data quality, governance, and compliance with industry standards.

• Monitor, troubleshoot, and resolve data pipeline and performance issues.

• Document data architecture, processes, and best practices.

• Support data migration and integration from various sources.

Qualifications:

• Bachelor’s degree in Computer Science, Information Technology, or a related field.

• 3 to 4 years of hands-on experience in data engineering or data services.

• Proven experience with AWS data services (e.g., S3, Glue, Redshift, Lambda).

• Strong expertise in Snowflake architecture, development, and optimization.

• Proficiency in SQL and Python for data manipulation and scripting.

• Solid understanding of ETL/ELT processes and data modeling.

• Experience with data integration tools and orchestration frameworks.

• Excellent analytical, problem-solving, and communication skills.

Preferred Skills:

• AWS Glue, AWS Lambda, Amazon Redshift

• Snowflake Data Warehouse

• SQL & Python

Skills: Aws Lambda, AWS Glue, Amazon Redshift, Snowflake Data Warehouse

Must-Haves

AWS data services (4-6 years), Snowflake architecture (4-6 years), SQL (proficient), Python (proficient), ETL/ELT processes (solid understanding)

Skills: AWS, AWS lambda, Snowflake, Data engineering, Snowpipe, Data integration tools, orchestration framework

Relevant 4 - 6 Years

python is mandatory

******

Notice period - 0 to 15 days only (Feb joiners’ profiles only)

Location: Kochi

F2F Interview 7th Feb

JOB DETAILS:

* Job Title: Tester III - Software Testing (Automation testing + Python + AWS)

* Industry: Global digital transformation solutions provide

* Salary: Best in Industry

* Experience: 4 -10 years

* Location: Hyderabad

Job Description

Responsibilities:

• Develop, maintain, and execute automation test scripts using Python.
• Build reliable and reusable test automation frameworks for web and cloud-based applications.
• Work with AWS cloud services for test execution, environment management, and integration needs.
• Perform functional, regression, and integration testing as part of the QA lifecycle.
• Analyze test failures, identify root causes, raise defects, and collaborate with development teams.
• Participate in requirement review, test planning, and strategy discussions.
• Contribute to CI/CD setup and integration of automation suites.

Required Experience:

• Strong hands-on experience in Automation Testing.
• Proficiency in Python for automation scripting and framework development.
• Understanding and practical exposure to AWS services (Lambda, EC2, S3, CloudWatch, or similar).
• Good knowledge of QA methodologies, SDLC/STLC, and defect management.
• Familiarity with automation tools/frameworks (e.g., Selenium, PyTest).
• Experience with Git or other version control systems.

Good to Have:

• API testing experience (REST, Postman, REST Assured).
• Knowledge of Docker/Kubernetes.
• Exposure to Agile/Scrum environment.

Skills: Automation testing, Python, Java, ETL, AWS

Microsoft Fabric, Power BI, Data modelling, ETL, Spark SQL

Remote work- 5-7 hours

450 Rs hourly charges

Key Responsibilities:

Design and develop ETL processes for claims, enrollment, provider, and member data

Handle EDI transactions (837, 835, 834) and health plan system integrations

Build data feeds for regulatory reporting (HEDIS, Stars, Risk Adjustment)

Troubleshoot data quality issues and implement data validation frameworks

Required Experience & Skills:

5+ years of ETL development experience

Minimum 3 years in Healthcare / Health Plan / Payer environment

Strong expertise in SQL and ETL tools (Informatica, SSIS, Talend)

Deep understanding of health plan operations (claims, eligibility, provider networks)

Experience with healthcare data standards (X12 EDI, HL7)

Strong knowledge of HIPAA compliance and PHI handling