50+ Data engineering Jobs in India

Data Engineer - Power BI Modelling

Level: Senior to Advanced - 5-15 years

Locations: Mumbai / Bengaluru

Joining an existing data engineering squad, you own Power BI semantic modeling on top of PySpark/SQL pipeline engineering, delivering enterprise-grade reporting and analytics. As an embedded Data Engineer, you turn PySpark pipelines into trusted Power BI models for the business.

Note: Shares a common PySpark/Snowflake base with "Data Engineer - Graphing" - source together, differentiate on semantic-modeling vs. graphing depth at interview.

Key responsibilities

1. Model semantically. Build and maintain Power BI semantic models (DAX, star schemas) for enterprise reporting.
2. Build pipelines. Develop PySpark/Python and SQL pipelines feeding the Snowflake environment.
3. Partner with stakeholders. Translate reporting requirements into performant data models.
4. Ensure data quality. Validate accuracy and performance of models and underlying pipelines.
5. Collaborate. Operate inside the existing squad with no separate delivery lead required.
6. Explore GenAI. Apply GenAI techniques to reporting and data use cases as opportunities arise.

Must-have qualifications

• Python and PySpark
• SQL
• Power BI semantic modeling (DAX, data modeling)

Preferred

• Snowflake; Dataiku
• GenAI exposure

What success looks like – first 6 to 12 Months

• Power BI models adopted for key reporting use cases
• Reliable PySpark/SQL pipelines feeding those models
• Smooth integration into the existing squad

Confidential – Wissen Technology

We're hiring a Tech Lead Azure Data Engineer on Microsoft Fabric, based in Bangalore and Mangalore.

Immediate joiners preferred.

Required Skills:

• 8+ years of data engineering experience.

• 5+ years of experience with Microsoft Azure Data Platform technologies.

• 3+ years of hands-on experience with Microsoft Fabric.

• Experience integrating Dynamics 365 and Salesforce environments.

• Experience working with large-scale enterprise datasets (>100M records preferred).

• Design and implement scalable data platforms using Microsoft Fabric.

• Develop and maintain Data Factory pipelines, Dataflows Gen2, Notebooks, and Lakehouse solutions.

• Create and optimize Medallion Architecture (Bronze, Silver, Gold) data models.

• Develop and manage Real-Time Analytics and Event Streaming solutions.

• Implement data governance, security, monitoring, and performance optimization within Fabric.

• Support Customer Insights implementation and customer profile analytics.

• Design and implement Salesforce Sales Cloud and Data Cloud integrations with Microsoft Fabric.

• Develop scalable ingestion frameworks for Salesforce objects and metadata.

• Create unified customer profiles by consolidating Salesforce, Dynamics 365, and external data sources.

• Develop AI-enabled data solutions using Microsoft Fabric AI capabilities, Copilot, Azure OpenAI, and Microsoft AI Services.

• Build AI-driven customer insights, predictive analytics, propensity models, and recommendations.

Data Engineer

Data Lakehouse & Platform Engineering 

About the Role

We are hiring Data Engineer to own the lifecycle of our enterprise Data Lakehouse platform. We are looking for engineers who think in systems, make platform-level design decisions, and can build and operate a production-grade, multi-source lakehouse from the ground up, covering ingestion through consumption across a complex, multi-cloud source landscape.

You will be the technical authority for a platform that consolidates data from 18+ enterprise products (Costpoint, GovWin, Specpoint, Vantagepoint, and others) into a governed, medallion-architected data lake on AWS S3 with Apache Iceberg table format, orchestrated via AWS Step Functions, and queryable through AWS Athena and Trino. This role is end-to-end: you own ingestion, transformation, quality, orchestration, ML data supply, and BI consumption.

Key Responsibilities

•      Architect and evolve the full medallion lakehouse — Bronze, Silver, and Gold layers — on AWS S3 with Apache Iceberg; own schema design, partitioning, compaction, and retention policies.

•      Design and implement scalable Glue ETL (PySpark) pipelines for bronze_to_silver and silver_to_gold transformations, incorporating dbt for SQL-layer transformations where appropriate.

•      Own and extend CDC ingestion via Fivetran; manage schema evolution, connector health, and sync reliability across 18+ source products.

•      Build and maintain AWS Step Functions state machines and EventBridge schedules for end-to-end pipeline orchestration; implement Lambda-based quality and drift monitors.

•      Govern the Glue Catalog and Lake Formation policies; enforce column-level security, row-level access controls, and audit logging to meet SOC2 and regulatory requirements.

•      Architect the query layer — optimize Athena workgroups and partition pruning; plan and execute Trino-on-EKS deployment for sub-second analytics workloads.

•      Partner with data science teams on SageMaker data supply: feature engineering pipelines, training dataset preparation, and model registry integration.

•      Implement real-time and near-real-time streaming solutions using Kafka or Kinesis where sub-13-minute latency is required.

•      Lead platform modernization initiatives: evaluate emerging formats (Iceberg vs. Delta Lake vs. Hudi), tooling, and cost optimization strategies.

•      Establish and enforce data engineering best practices: code reviews, CI/CD for pipeline code, IaC (Terraform / CloudFormation), and incident response runbooks.

•      Mentor and level up junior and mid-level data engineers; define team standards for pipeline design, testing, and documentation.

Required Qualifications

•      Software or data engineering experience, with at least 4 years in an architect or technical lead capacity designing large-scale cloud data platforms.

•      Deep, hands-on expertise with AWS data services: S3, Glue (PySpark ETL), Athena, Step Functions, Lambda, EventBridge, Lake Formation, SageMaker, and CloudWatch.

•      Production experience with Apache Iceberg (or Delta Lake / Hudi) table formats — compaction, snapshot management, schema evolution, and time travel.

•      Strong PySpark and Python skills; ability to write, review, and optimize distributed data processing jobs at scale.

•      Hands-on experience with CDC-based ingestion platforms (Fivetran, Debezium, or equivalent) across heterogeneous source systems.

•      Proven experience designing and implementing medallion (Bronze/Silver/Gold) or equivalent multi-hop lakehouse architectures.

•      Experience with data pipeline orchestration: AWS Step Functions, Apache Airflow, or equivalent; event-driven pipeline design patterns.

•      Strong SQL skills; experience with Athena, Trino, Presto, or equivalent query engines for large-scale analytical workloads.

•      Familiarity with data governance tooling: catalog management (Glue Catalog, Apache Polaris/Iceberg REST), data lineage, access controls, and audit frameworks.

•      Experience with Infrastructure as Code (Terraform or CloudFormation) for data platform provisioning and drift management.

•      Solid understanding of dimensional modeling, schema design (star/snowflake), and data normalization for BI and analytics workloads.

•      Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent professional experience.

Preferred Qualifications

•      Experience operating Trino or PrestoDB on Kubernetes (EKS); tuning for sub-second query latency and multi-tenant workloads.

•      Familiarity with streaming platforms (Kafka, Kinesis, or Pub/Sub) and real-time lakehouse patterns.

•      Experience with Apache Polaris or other Iceberg REST catalog implementations.

•      Exposure to SageMaker MLOps pipelines, Model Registry, and feature store patterns for ML data supply.

•      Experience with dbt (data build tool) for SQL-layer transformation and documentation in lakehouse environments.

•      Government contracting or ERP domain knowledge (Costpoint, Deltek, Oracle, or similar enterprise platforms) is a strong plus.

•      AWS certifications: Data Engineer Associate, Solutions Architect Professional, or equivalent.

What You Will Build

You will be a founding architect of a strategic, cross-product data platform that serves 18+ enterprise applications and their analytics, ML, and AI workloads. The decisions you make on schema, storage format, query layer, governance, and orchestration will shape the data foundation of the company for years. This is a high-impact, high-ownership role with direct visibility to senior leadership.

Job Summary

We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.

Technical Skills

• Strong hands-on experience in Python and PySpark development.
• Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
• Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
• Experience with Power BI Data Modeling and Semantic Layer development.
• Proficiency in DAX (Data Analysis Expressions).
• Experience designing and managing Semantic Models in Power BI.
• Strong SQL skills and experience working with large datasets.
• Knowledge of data warehousing concepts and best practices.

Preferred Skills

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to modern data platforms like Databricks.
• Understanding of data governance and data quality frameworks.

Role Summary

Seeking an experienced SQL Developer with strong expertise in Data Lake architecture, Data Engineering, AI/ML data modelling, and Vector Database design. The candidate will be responsible for building scalable data platforms, developing optimized SQL solutions, designing AI-ready data models, and supporting enterprise analytics and GenAI initiatives.

Key Responsibilities

• Design, develop, and optimize complex SQL queries, stored procedures, views, and database objects.
• Build, maintain, and govern enterprise Data Lakes for structured, semi-structured, and unstructured data.
• Design scalable data models for Analytics, Machine Learning (ML), and AI applications.
• Develop and maintain data ingestion, transformation, and data preparation pipelines.
• Architect and manage Vector Database solutions supporting GenAI, semantic search, embeddings, and RAG-based applications.
• Ensure data quality, security, performance, governance, and scalability across platforms.
• Integrate data from multiple enterprise systems, databases, APIs, and business applications.
• Collaborate with Business, Analytics, Data Science, and AI teams to deliver enterprise data solutions.

Mandatory Skills

• Advanced SQL Development (SQL Server, PostgreSQL, Oracle, MySQL, etc.)
• Data Lake Architecture, Development, and Maintenance
• Data Warehousing & Dimensional Data Modelling
• AI/ML Data Modelling and Feature Engineering
• Python for Data Engineering, Data Processing, and Automation
• Query Performance Tuning & Database Optimization
• Data Governance & Data Quality Management
• Vector Database Architecture and Management (Pinecone, Qdrant, Weaviate, Milvus, Chroma, or similar)
• Experience handling large-scale structured and unstructured datasets

Preferred Skills

• Experience with GenAI, RAG (Retrieval-Augmented Generation), Embeddings, and LLM-based applications
• PySpark and Distributed Data Processing
• Power BI or Enterprise Reporting Platforms
• Knowledge of MLOps, AI data pipelines, and modern data architectures

Key Attributes

• Strong analytical and problem-solving skills
• Ability to independently own end-to-end data platform solutions
• Excellent communication and stakeholder management skills
• Passion for Data Engineering, AI, ML, and GenAI technologies

Ideal Candidate

A hands-on SQL Developer who can build and maintain enterprise Data Lakes, design AI/ML-ready data models, develop Python-based data solutions, and architect Vector Database platforms to support advanced analytics, AI, and GenAI initiatives.

Job Description:

Position: Senior Data Engineer

Location: Chennai / Pune / Bangalore / Hyderabad

Working Type: WFO

Shift: UK Shift (2:00 – 11:00 PM)

Experience : 7+ years overall

Interviews: Assessment || 2 Interview rounds.

Notice Period: Immediate Joiner

Key Responsibilities

Implement ingestion, transformation, and optimization of enterprise data sources into Microsoft Fabric Lakehouse environments.

Configure and optimize Fivetran connectors (Oracle, SQL DB, etc.)

Manage large-volume ingestion and backfill operations

Implement Bronze to Silver transformation pipelines

Develop incremental load and CDC logic

Optimize Lakehouse performance and storage patterns

Implement monitoring (record counts, load duration, failure tracking)

Support Dev/Test/Prod promotion processes

Required Qualifications

7+ years of data engineering experience

Hands-on experience with Microsoft Fabric or Azure Synapse/Data Factory

Strong experience with Fivetran or similar ELT tools

Experience handling high-volume datasets (hundreds of millions of records)

Proficiency in SQL, Python, and data modeling concepts

Strong understanding of Medallion architecture.

Roles & Responsibilities

• Design, develop, and deliver scalable end-to-end data pipelines using Azure Data Factory, ensuring robust integration

of enterprise-wide data from diverse sources

• Build and optimize data engineering workflows using Databricks and PySpark

• Write efficient, high-performance SQL for data transformation and analysis

• Work with the Azure Cloud platform and associated services, applying strong understanding of data warehousing,

data models, and pipelines

• Provide technical leadership to a team of developers, including code reviews and enforcing best practices across the

development lifecycle

• Oversee CI/CD implementation using Azure DevOps, managing deployments across development, QA, and production

environments with proper change control processes

• Collaborate with cross-functional teams to translate business requirements into scalable data solutions

• Ensure data quality, reliability, and performance across all pipelines and platforms

Ideal Candidate

1Strong Azure Databricks Engineer / Senior Data Engineer Profile

2Mandatory (Experience 1) – Must have minimum 8+ years of overall experience in Data Engineering, Data Development, or related data technology roles, with strong hands-on experience in enterprise data pipeline development.

3Mandatory (Experience 2) – Must have strong hands-on experience with Azure Databricks, including development and optimization of scalable data engineering workflows using Databricks and PySpark.

4Mandatory (Experience 3) – Must have strong hands-on proficiency in PySpark/Python and SQL, with proven experience developing complex data transformations, processing workflows, and performance-optimized queries.

5Mandatory (Experience 4) – Must have hands-on experience with Azure Data Factory (ADF) for designing, developing, and orchestrating end-to-end data pipelines and integrating data from multiple sources.

6Mandatory (Experience 5) – Must have strong experience working on the Azure Cloud platform and associated data services, with solid understanding of data warehousing, data modeling, pipeline architecture, and enterprise data solutions.

7Mandatory (Experience 6) – Must have hands-on experience implementing CI/CD using Azure DevOps, including deployment and release management across development, QA, and production environments.

8Mandatory (Experience 7) – Must have proven technical leadership experience, including code reviews, enforcing development best practices, mentoring developers, and providing technical guidance to a data engineering team.

9Mandatory (Notice Period) – Immediate joiners or candidates who can join within 15 days.

10Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

Experience: 4–6 Years

Domain: Automotive IoT | Connected Vehicles | Firmware & OTA

Core Stack: Rust | AWS | IoT Core | Apache Kafka | MemoryDB

Key Responsibilities  

OTA & Firmware Lifecycle  

• Co-own OTA firmware rollout operations across multi-ECU connected vehicle architectures.
• Design automated mechanisms to detect update failures, network interruptions, verification errors, and stalled deployments.
• Implement deterministic retry, recovery, and rollback mechanisms to ensure reliable firmware updates without vehicle bricking.
• Ensure firmware package integrity, signature validation, and data security throughout the OTA pipeline.

Data Engineering & Telemetry  

• Design and maintain real-time streaming pipelines for vehicle telemetry, heartbeats, OTA campaign status, and ECU state changes.
• Build high-throughput data services and workers using Rust for payload routing, processing, and verification.
• Use Apache Kafka and AWS IoT Core for real-time data ingestion and event streaming.
• Leverage AWS MemoryDB for Redis for low-latency fleet state, campaign progression, and session management.
• Build resilient pipelines capable of handling intermittent connectivity, noisy networks, and out-of-order data.

Monitoring & Analytics  

• Build real-time dashboards for fleet health, firmware versions, OTA campaigns, and update progress.
• Define and monitor key OTA metrics including success/failure rates, retry rates, failure categories, and completion time.
• Implement automated alerting and anomaly detection for unexpected failure spikes during staged or canary rollouts.
• Analyze logs, traces, and telemetry data to identify campaign bottlenecks, telemetry loss, and hardware-related failures.

Required Skills  

Mandatory  

• 4–6 years of experience in Data Engineering, Software Engineering, or IoT Backend Engineering.
• Strong hands-on experience with Rust for backend/data processing applications.
• Experience with AWS, particularly IoT Core, S3, ECS/EKS, and Lambda.
• Strong experience with Apache Kafka and real-time data pipelines.
• Hands-on experience with AWS MemoryDB for Redis or Redis Enterprise.
• Working knowledge of Python and SQL.
• Experience with MQTT, WebSockets, and HTTP/S protocols.
• Strong understanding of distributed systems, streaming data, and resilient data pipelines.

Preferred  

• Experience with firmware lifecycle management and OTA systems.
• Exposure to connected vehicles, automotive IoT, telemetry platforms, or connected hardware fleets.
• Experience with device shadows and fleet/device state management.
• Experience building telemetry dashboards using Power BI, Apache Superset, or custom dashboards.
• Experience with staged/canary deployments and automated failure recovery.

Primary Technology Stack  

• Languages: Rust, Python, SQL
• Cloud: AWS, IoT Core, S3, ECS/EKS, Lambda
• Streaming: Apache Kafka
• Caching & State: AWS MemoryDB for Redis, Redis
• IoT Protocols: MQTT, WebSockets, HTTP/S
• Analytics & Visualization: Power BI, Apache Superset
• Domain: Automotive IoT, Vehicle Telemetry, OTA, Firmware Management

Strong Databricks Architect Profile with end-to-end Lakehouse ownership

2

Mandatory (Experience 1) – Must have 10+ years of software engineering experience with atleast 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development.

3

Mandatory (Experience 2) – Must have atleast 5+ years of expertise across the Databricks ecosystem — Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog

4

Mandatory (Tech skill 1) – Must have worked at architecture level, owning end-to-end design through deployment

5

Mandatory (Tech skill 2) – Must have strong experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability

6

Mandatory (Tech skill 3) – Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems

7

Mandatory (AI Exposure) – Must have at least a basic working understanding of how AI services or tools work

8

Mandatory (Communication) – Must have strong stakeholder management & requirement-gathering experience with US or UK clients

9

Mandatory (Company) – Must come from a B2B IT services or IT consulting background

10

Mandatory (Note) – CTC is inclusive of 5% variable

11

Preferred (Tech skill 1) – Azure Databricks or Azure data services experience (project runs on Azure DevOps)

12

Preferred (Tech skill 2) – MLflow or MLOps practices and AI use cases (RAG, AI/BI)

13

Preferred (Tech skill 3) – CI/CD, Databricks Asset Bundles (DABs) or equivalent packaging, Terraform or IaC, reusable deployment templates

14

Preferred (Integrations) – ServiceNow or enterprise system integrations

15

Preferred (Certifications) – Databricks (Data Engineer Associate or Professional, ML or GenAI tracks), Azure or AWS cloud certifications

Skills Referential (Required knowledge, skills and abilities)

Technical Skills:

Python

Pyspark

SQL

ETL Aws, Azure, gcp

Key Responsibilities  

• Design, build, and optimize scalable data pipelines for AI/ML applications.
• Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
• Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
• Fine-tune open-source and foundation models using domain-specific datasets.
• Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
• Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
• Develop APIs and AI services for production deployment.
• Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
• Monitor model performance, troubleshoot production issues, and maintain technical documentation.

Required Skills  

Mandatory  

• 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
• Strong programming skills in Python and SQL.
• Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
• Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
• Experience in LLM fine-tuning and working with Hugging Face models.
• Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
• Experience with Git, REST APIs, Linux environments, and data processing libraries.

Preferred  

• Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
• Familiarity with Docker, Kubernetes, and MLflow.
• Exposure to Apache Spark or Airflow for data engineering workflows.
• Experience with cloud platforms (AWS, Azure, or GCP).

Primary Technology Stack  

• Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
• AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
• Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
• Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
• Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
• Vector Databases: Pinecone, Chroma, Milvus, Weaviate
• Databases: PostgreSQL, MongoDB
• MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
• Cloud Platforms: AWS, Azure, GCP

Experience: 1–3 Years

Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps

About AuxoAI:

AuxoAI is a global platform-based services firm. We help companies—turn their strategies into practical digital and AI solutions. By understanding how our clients make decisions, we use digital and Artificial Intelligence (AI) technologies to drive growth, enhance their operations, improve customer experiences, and provide clear, actionable insights from their data. What We Do We work across various industries such as healthcare, high-tech, consumer packaged goods (CPG), finance etc., and in sales, marketing, and customer support functions.

We help our clients with accelerating their digital and AI journeys through:

• AI Application Development

• Data, Digital and Cloud acceleration using AI

• AI Native Product Engineering

We are seeking a skilled and experienced Data Engineer to join our dynamic team. The ideal candidate will have 6+ years of prior experience in data engineering, with a strong background in AWS (Amazon Web Services) technologies. This role offers an exciting opportunity to work on diverse projects, collaborating with cross-functional teams to design, build, and optimize data pipelines and infrastructure.

Responsibilities:

* Design, develop, and maintain scalable data pipelines and ETL processes leveraging AWS services such as S3, Glue, EMR, Lambda, and Redshift.

* Collaborate with data scientists and analysts to understand data requirements and implement solutions that support analytics and machine learning initiatives.

* Optimize data storage and retrieval mechanisms to ensure performance, reliability, and cost-effectiveness.

* Implement data governance and security best practices to ensure compliance and data integrity.

* Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring.

* Stay abreast of emerging technologies and industry trends, recommending innovative solutions to enhance data engineering capabilities.

Requirements :

* Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

* 6+ years of prior experience in data engineering, with a focus on designing and building data pipelines.

* Proficiency in AWS services, particularly S3, Glue, EMR, Lambda, and Redshift.

* Strong programming skills in languages such as Python, Java, or Scala.

* Experience with SQL and NoSQL databases, data warehousing concepts, and big data technologies.

* Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools (e.g., Apache Airflow) is a plus.

Immediate joiner only

Create and optimize Medallion Architecture (Bronze, Silver, Gold) data models.

Required Skills:

• 8+ years of data engineering experience.

• 5+ years of experience with Microsoft Azure Data Platform technologies.

• 3+ years of hands-on experience with Microsoft Fabric.

• Experience integrating Dynamics 365 and Salesforce environments.

• Experience working with large-scale enterprise datasets (>100M records preferred).

• Design and implement scalable data platforms using Microsoft Fabric.

• Develop and maintain Data Factory pipelines, Dataflows Gen2, Notebooks, and Lakehouse solutions.

• Create and optimize Medallion Architecture (Bronze, Silver, Gold) data models.

• Develop and manage Real-Time Analytics and Event Streaming solutions.

• Implement data governance, security, monitoring, and performance optimization within Fabric.

• Support Customer Insights implementation and customer profile analytics.

• Design and implement Salesforce Sales Cloud and Data Cloud integrations with Microsoft Fabric.

• Develop scalable ingestion frameworks for Salesforce objects and metadata.

• Create unified customer profiles by consolidating Salesforce, Dynamics 365, and external data sources.

• Develop AI-enabled data solutions using Microsoft Fabric AI capabilities, Copilot, Azure OpenAI, and Microsoft AI Services.

• Build AI-driven customer insights, predictive analytics, propensity models, and recommendations.

Description:

Analytical Engineer with strong Data Analyst and Data Modelling expertise required to translate business requirements into structured, analytics-ready datasets. Must have experience in Data Vault 2.0 / dimensional modelling and advanced SQL for data transformation and analysis. Role focuses on data profiling, validation, and delivery of trusted data for reporting and analytics. Experience with Azure/Databricks and enterprise data environments preferred. Strong stakeholder engagement and ability to bridge business and technical data requirements essential.

Must Have Skills

• Data Analysis
• Data Governance
• Data Modeling tool

Nice to Have Skills

• Business writing skills
• Governance, Risk and Controls
• Principles of project management
• Relevant regulatory knowledge
• Relevant software and systems knowledge

Job Title: Data Engineer – PySpark | Oracle | GCP

Experience: 5–7 Years

Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Job Summary

We are seeking an experienced Data Engineer with strong expertise in PySpark, Oracle, and Google Cloud Platform (GCP) to design, develop, and optimize scalable data pipelines. The ideal candidate should have hands-on experience in ETL development, data integration, and cloud-based data engineering solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/data pipelines using PySpark.
• Extract, transform, and load data from Oracle databases into GCP environments.
• Build and optimize batch data processing workflows for high performance and reliability.
• Develop data engineering solutions using GCP services.
• Ensure data quality through validation, monitoring, and troubleshooting.
• Optimize SQL queries and ETL jobs for performance and scalability.

Required Skills

• 5–7 years of experience as a Data Engineer.
• Strong hands-on experience with PySpark.
• Solid experience with Oracle Database and advanced SQL.
• Hands-on experience with Google Cloud Platform (GCP).
• Strong understanding of ETL processes and data warehousing concepts.

Work Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Title                                 : Snowflake Cortex AI Engineer

Experience                    : 5+ years

Location                         : Remote

Work type                      : Remote

Employment Type      : Full Time

Notice Period               : Immediate

Work Day                      : Mon to Fri

About the Role:

We are seeking a highly skilled Snowflake Cortex AI Engineer with 5+ years of experience in Data Engineering, AI, and Snowflake. The ideal candidate will have hands-on expertise in Snowflake Cortex AI, Snowpark, and Generative AI capabilities to build intelligent, scalable, and secure AI-powered data applications. The role involves designing AI-driven solutions, integrating LLM capabilities into enterprise workflows, and collaborating with cross-functional teams to deliver business value.

Key Responsibilities:

• Design, develop, and implement AI-powered solutions using Snowflake Cortex AI.
• Build intelligent data applications leveraging Snowpark, Cortex AI functions, and SQL.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise data stored in Snowflake.
• Integrate Large Language Models (LLMs) into enterprise applications using Snowflake Cortex.
• Design and optimize AI workflows for document summarization, sentiment analysis, classification, translation, question answering, and text generation.
• Develop scalable data pipelines to support AI and machine learning workloads.
• Collaborate with Data Engineers, Data Scientists, and business stakeholders to understand AI use cases and deliver effective solutions.

Ensure AI solutions comply with enterprise security, governance, and data privacy standards.

• Optimize Snowflake performance and AI workloads for scalability and cost efficiency.
• Participate in architecture discussions, code reviews, and technical documentation.

Required Skills & Experience

• 5+ years of experience in Data Engineering, AI/ML, or Analytics.
• Strong hands-on experience with Snowflake.
• Experience working with Snowflake Cortex AI capabilities.
• Strong understanding of Snowpark (Python or SQL).
• Experience building AI-powered applications using enterprise data.
• Hands-on experience with Python and SQL.
• Knowledge of Generative AI, Prompt Engineering, and Large Language Models (LLMs).
• Experience designing and implementing RAG (Retrieval-Augmented Generation) solutions.
• Strong understanding of data modeling and data warehousing concepts.
• Experience developing and optimizing ETL/ELT pipelines.
• Experience integrating REST APIs and external AI services. 

Technical Skills

• Snowflake
• Snowflake Cortex AI
• Snowpark
• SnowSQL
• Snowpipe
• Streams & Tasks
• Secure Data Sharing
• Performance Optimization
• Role-Based Access Control (RBAC) 

Programming

• Python
• SQL
• AI & Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Model Integration
• Text Analytics
• Semantic Search
• Data Engineering
• ETL/ELT Development
• Data Warehousing
• Data Pipelines
• Structured & Semi-Structured Data Processing
• Cloud (Preferred)
• AWS / Azure / GCP

Preferred Skills

• Experience with vector search and semantic search concepts.
• Knowledge of Snowflake Cortex Analyst, Cortex Search, or Cortex Agents.
• Experience with AI governance and responsible AI practices.
• Familiarity with LangChain, LangGraph, or similar AI orchestration frameworks.
• Exposure to ML model deployment and MLOps practices.
• SnowPro certification is an added advantage. 

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related field.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to translate business requirements into AI-driven solutions.
• Strong collaboration and teamwork skills.
• Self-driven with the ability to work independently in a remote environment.

Job description

We are seeking an experienced and highly skilled Workato Developer to lead integration efforts between Salesforce, enterprise data warehouses, and other source systems. The ideal candidate will have deep experience in ETL processes, data migration, and ongoing data synchronization using Workato. You will be responsible for building scalable, secure, and performant integrations for a large enterprise environment. Key Responsibilities:

• Design, develop, and maintain data integrations between Salesforce and external systems (e.g., data warehouse, ERP, marketing platforms) using Workato. • Implement ETL workflows for both one-time data migrations and ongoing bi-directional data sync. • Create, configure, and maintain recipes, connections, and custom connectors in Workato. • Collaborate with Salesforce developers, data engineers, and business analysts to understand integration needs and translate them into technical solutions. • Monitor and optimize performance of integration flows and ensure data accuracy, error handling, and logging. • Develop and maintain integration documentation, data mapping, and transformation logic. • Work closely with data warehouse teams to enable seamless data exchange and transformation. • Support data quality initiatives by identifying data anomalies and recommending corrective actions. • Ensure compliance with enterprise security and data governance standards. Required Qualifications:

• 5–7 years of professional experience in data integration, ETL, or data engineering roles. • Hands-on experience with Workato or similar iPaaS platforms (e.g., MuleSoft, Boomi, SnapLogic) with proven track record of successful Salesforce integrations. • Strong expertise in integrating Salesforce with other enterprise systems (ERP, marketing tools, databases, APIs, etc.). • Proficiency in data transformation, JSON/XML, REST/SOAP APIs, and SQL. • Experience working in large enterprise environments with complex data landscapes. • Solid understanding of Salesforce data model, objects, and API limits. • Familiarity with data warehousing concepts, such as dimensional modeling, data marts, and analytics pipelines. • Bachelor’s degree in Computer Science, Information Systems, or a related field. Preferred Qualifications:

• Workato Certification or similar iPaaS certifications. • Experience with CI/CD, version control (Git), and Agile methodologies. • Exposure to Salesforce Data Loader, Salesforce Connect, or external object configurations

At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.

For over 35 years, the experts at Mitratech have been focused on solving the complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries.

As we continue to grow, we’re always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Mitratech a truly exceptional place to work!

About Engineering at Mitratech Legal Solutions 

Mitratech's engineering organization is a collaborative and dynamic environment where engineers are empowered to drive technical direction and innovation. Our engineers are passionate about delivering high-quality products and solutions that meet the evolving needs of our customers, and we're committed to fostering a culture of continuous learning and growth. 

About the Role 

Mitratech is a fast-paced and dynamic environment, and this role requires someone who is adaptable, resilient, and able to thrive in a rapidly changing landscape. If you’re a seasoned engineer with a passion for technical leadership, innovation, and collaboration — including building the data foundations that power both reporting and AI-driven products — we’d love to hear from you. 

What You Will Do

• Drive technical direction for a significant product domain or platform capability, ensuring alignment with business objectives and customer needs 
• Design and maintain data pipelines that increasingly feed AI systems (e.g., RAG ingestion, embeddings, vector stores), not just dashboards and reports 
• Use AI-assisted engineering tools (e.g., Claude Code, Copilot, Cursor) as part of your own workflow, and help other engineers adopt them effectively 
• Reduce systemic complexity by identifying and leading architectural debt remediation, and develop strategies for ongoing technical debt management 
• Partner with Product and Engineering leadership to inform multi-quarter roadmap feasibility, and provide technical guidance and oversight to ensure successful implementation 
• Elevate engineering craft across multiple teams through RFCs, mentorship, and knowledge sharing, and develop training programs to improve engineering skills and knowledge 
• Represent Mitratech’s technical capabilities externally, including speaking at conferences, contributing to open-source projects, and engaging with industry peers and thought leaders 

 What We Are Looking For 

To be successful in this role, you will need: 

• 10+ years of experience in software engineering, with a focus on technical leadership and architecture 
• Deep understanding of data engineering principles, including data modeling, data warehousing, reporting, and data governance 
• Strong technical expertise in SQL, PostgreSQL, ETL/ELT pipelines, BI tools, and analytics platforms 
• Comfort using AI coding assistants (Claude Code, Copilot, Cursor, or similar) as a regular part of the engineering workflow 
• Practical experience with AI/LLM-adjacent data work — e.g., RAG ingestion pipelines, embedding generation, vector store management, or applying data quality and governance practices to data consumed by AI models 
• 5+ Years of experience on Ruby on Rails, React, and modern cloud platforms such as AWS 
• Experience leading cross-functional initiatives with product, engineering, analytics, and business teams 

Nice to Have 

• Experience with a semantic or metrics layer (e.g., dbt Semantic Layer, headless BI) 
• Experience with BI, reporting, dashboards, and customer-facing analytics 
• Understanding of CI/CD, Git-based workflows, and infrastructure-as-code 

The Stack Context 

• Modern data stack: Fivetran, Airbyte, dbt, Snowflake, GitHub, Terraform, or similar tools 
• Application context: Ruby on Rails, React, or similar backend/frontend frameworks 
• Data modeling: SQL, analytics models, documentation, testing, naming standards, and version control 
• Infrastructure: cloud-based data infrastructure, infrastructure-as-code, CI/CD, monitoring, and cloud storage 
• Data workflows: ingestion, transformation, orchestration, reporting, deployment, and change management 
• Reporting focus: trusted metrics, scalable reporting models, dashboards, exports, and data quality 
• AI surface: data pipelines and quality practices supporting AI/LLM use cases (RAG, embeddings, vector stores) alongside traditional BI 

Why This Role 

This role offers a unique opportunity to drive technical direction and innovation at a rapidly growing company, while also mentoring and coaching engineers to improve their craft. As a Principal Data Engineer at Mitratech, you will have the chance to work on complex and challenging problems, collaborate with cross-functional teams, and represent the company’s technical capabilities externally. If you’re looking for a role that offers a mix of technical leadership, innovation, and collaboration, this could be the perfect fit for you. 

We are an equal-opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability, or veteran status.

Job Title: Sales Engineer

We are hiring a Sales Engineer to drive business growth by promoting and selling engineering products and technical solutions to industrial and commercial clients. The ideal candidate will combine technical knowledge with strong sales and relationship-building skills to understand customer requirements and recommend suitable solutions.

Key Responsibilities:

• Identify and develop new business opportunities through field visits and client meetings.
• Promote engineering products and provide technical product demonstrations.
• Understand customer requirements and prepare quotations and proposals.
• Negotiate contracts and close sales while ensuring customer satisfaction.
• Build and maintain long-term relationships with customers and channel partners.
• Coordinate with internal teams for order execution and after-sales support.
• Maintain accurate sales reports, customer records, and market feedback.
• Achieve assigned sales targets and contribute to business growth.

Requirements:

• Bachelor's degree in Mechanical, Electrical, Civil, Instrumentation Engineering, or a related field.
• 2–5 years of experience in technical or engineering sales (freshers with relevant technical knowledge may also apply).
• Strong communication, negotiation, and presentation skills.
• Ability to read technical drawings and specifications.
• Proficiency in MS Office and CRM software.
• Valid driving license and willingness to travel extensively for business development. 

15+ years of IT Delivery and Technology Services experience.

15+ years managing large offshore delivery organizations.

Proven leadership of portfolios exceeding 200+ resources.

Financial Services or Banking experience is a MUST

Extensive technical experience in Data Engineering, Data Platforms, Data Brics.

Cloud Data Transformation Programs, Data Warehousing, Azure Data Platform and compliance experience

Job Title : Data Platform Engineer (SDE 2 / SDE 3)

Experience : 3 to 8 Years (SDE2 : 3 to 5 Years | SDE3 : 5.5 to 8 Years)

Location : Remote (Contract) → Pune (Post Conversion)

Employment Type : Contract-to-Hire (3 Months)

Mandatory Skills :

Java / Python / Go, SQL, Data Engineering, ETL / ELT, Apache Airflow, Apache Spark / Flink, Kafka, AWS/GCP/Azure, Distributed Systems, API Development, Docker, Kubernetes, CI/CD, System Design

Role Overview :

We are looking for a Data Platform Engineer with strong backend engineering expertise to build scalable, high-performance data platforms and distributed systems. The ideal candidate should have hands-on experience in developing production-grade backend applications along with designing and maintaining modern data pipelines.

Key Responsibilities :

• Build and maintain scalable ETL/ELT and data processing pipelines.
• Develop backend services and APIs using Java (preferred), Python, or Go.
• Design batch and real-time data pipelines using Spark, Flink, Kafka, and Airflow.
• Optimize SQL queries, data models, and distributed data systems.
• Work with cloud platforms (AWS, GCP, or Azure) and container technologies (Docker, Kubernetes).
• Implement CI/CD, monitoring, logging, and performance optimization.
• Collaborate with product and engineering teams on scalable system design and architecture.

Required Qualifications :

• SDE 2 : 3 to 5 years of Backend + Data Engineering experience.
• SDE 3 : 5.5 to 8 years of Backend + Data Engineering experience.
• Strong coding skills in Java (preferred), Python, or Go.
• Excellent SQL and data modeling knowledge.
• Hands-on experience with Airflow, Apache Spark / Apache Flink, or similar technologies.
• Experience building scalable backend services and APIs.
• Good understanding of distributed systems, system design. and scalable architecture.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Hands-on experience with Docker and Kubernetes.
• Strong understanding of CI/CD pipelines.
• Excellent problem-solving and debugging skills.

Good to Have :

• Experience with Snowflake, BigQuery, or Redshift.
• Understanding of Kafka, Kinesis, or Pub/Sub
• Performance optimization of large-scale distributed systems
• FinTech or high-scale distributed systems experience.
• Knowledge of data governance, security, and compliance.

Preferred Candidate Profile :

• Strong Backend + Data Engineering experience
• Experience building scalable production systems
• Strong ownership mindset
• Good system design knowledge
• Experience processing millions of events using Kafka/Spark
• Built APIs supporting data infrastructure
• Production engineering experience

Interview Process :

1. Take-home Coding Assignment (48 Hours)
2. Leadership & Strategy Round (1 Hour)
3. Technical Depth – Data Engineering & Performance (1 Hour)
4. Culture & Values Fit (30 Minutes)

Requirements:

• 5+ years in data architecture/data engineering, with at least 2+ years in an architect or lead capacity.
• Strong SQL: advanced query optimisation, indexing, partitioning strategies.
• Data modeling dimensional modelling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design.
• Cloud data platforms: hands-on with AWS (Redshift, S3 Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
• Big data ecosystems: Spark, Hadoop, or Kafka for large-scale/streaming data.
• Data warehousing Snowflake, Redshift, BigQuery, or Databricks.
• ETL/ELT pipeline design: Airflow, dbt, Fivetran, or similar orchestration tools.
• Data governance & security: data lineage, access control, compliance (GDPR/SOC2), master data management.

Strongly Preferred:

• Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows).
• Programming in Python or Scala for pipeline development.
• API/microservices architecture exposure, understanding how data systems integrate with application layers.
• Experience with data mesh/data lake house architectures.
• Prior experience presenting architecture decisions to leadership/stakeholders.

Nice-to-Have (Differentiators):

• Certifications: AWS/GCP/Azure data architecture certs.
• Experience in a high-growth startup (built systems from scratch, not just maintained legacy).
• Exposure to real-time/streaming architecture (Kafka, Kinesis, Flink).

Hiring for Engineering Manager – Data Engineering

Exp : 10 - 15 yrs

Work Location : Pune WFO

Must Have Skills :

10+ years of experience in Data Engineering.

3+ years of experience leading Data Engineering teams.

Must be a Manager

Strong hands-on experience with Databricks on Azure. 

Strong expertise in PySpark, Spark SQL, Python, and SQL.

Hands-on experience with Delta Lake, Delta Live Tables (DLT), Unity Catalog, Databricks Workflows, and Auto Loader.

Experience designing and building enterprise-scale data platforms.

Strong knowledge of ETL/ELT, Medallion Architecture, and batch & streaming data pipelines.

About the Role

CLOUDSUFI, a Google Premium Partner specializing in data and AI solutions, is seeking a Staff / Principal Tech Lead to drive the technical execution of our Google Data Commons program. This is a high-visibility, horizontal leadership role embedded within the Google ecosystem — based physically at Google's Bangalore office — working in close daily collaboration with Google's core Data Commons engineering team. The Tech Lead is the technical spine of the engagement. They sit across all four delivery areas — Data Engineering, Frontend, ML/AI, and DevOps/Infrastructure — providing architectural direction, resolving cross track dependencies, and ensuring the quality and coherence of everything we ship. Equally critical is the ability to represent CloudSufi in a credible, articulate, and collaborative manner to Google counterparts at every level.

This is a genuinely hands-on role: the successful candidate must be able to write, debug, and reason about Python and GCP code themselves — not only direct others or lean on AI coding assistants — and must be comfortable operating in an open-source, public-data environment without relying, for example, on Google internal (google3) tooling.

Technology Stack & Domain Knowledge

Core / Must-Have

• Relevant experience on Knowledge Graph, Statistical Data and Analytics (any experience with Google Data Commons is a nice to have, but not required)

• Google Cloud Spanner – schema design, distributed transactions, interleaved tables, and performance tuning at scale.

• Google BigQuery – data modeling, partitioning/clustering strategies, query optimization, and integration with downstream consumers.

• Data pipelines – Apache Beam / Dataflow, or equivalent GCP-native ETL tooling.

• Infrastructure as Code – Terraform on GCP; Cloud Build, Artifact Registry, GKE or Cloud Run.

• Demonstrable, autonomous hands-on proficiency in Python and native GCP tooling — able to code and debug independently in a live technical discussion, with AI-assisted development as a complement to (not a replacement for) that proficiency.

• Direct experience working with open, public, and unstructured datasets (e.g. sourcing, cleaning, and integrating public statistics or open data feeds) using open-source or standard GCP-native tooling.

• Solid grounding in knowledge graph vs data warehouse principles, and how to design for schema and data drift in an open-source knowledge graph context.

• CI/CD experience and working with GitHub

• Full stack experience, specially with data centric apps/systems Strong Advantage

• Python (primary language for Data Commons import tooling and ML pipelines).

• TypeScript / React for the Data Commons web frontend and visualization layers.

• Vertex AI, BigQuery ML, or equivalent ML lifecycle tooling.

• Knowledge graph principles, RDF/SPARQL, or statistical data modeling.

• DataCommons Python / REST APIs and the DCID import automation tools.

• Experience with Google's internal engineering culture, tools (e.g. Buganizer, Critique, Cider), or prior delivery inside a Google product or partnership engagement.

Experience & Qualifications Required

• 10+ years of software engineering experience, with at least 3 years in a formal or informal tech lead capacity overseeing multiple workstreams.

• Demonstrable experience delivering production-grade systems on Google Cloud Platform.

• Prior experience working with or for Google — as a Googler, through a Google partnership program, or as a contractor embedded in a Google team — is strongly preferred.

• Exceptional verbal and written English communication skills; able to engage confidently with senior Google engineers and program managers.

• Proven ability to operate across ambiguous, fast-moving programs with multiple parallel tracks.

• Based in Bangalore, India, and able to work on-site at Google's Bangalore office on a regular basis.

• Able to read and interpret an RFP / SOW and connect its terms to a workable technical delivery plan. 

Job Summary

We are seeking a motivated Data Engineer with strong skills in SQL, Python, and Linux to design, build, and maintain scalable data pipelines and support data-driven decision-making. The ideal candidate should have experience working with large datasets, ETL processes, and relational databases while ensuring data quality and performance.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Write optimized SQL queries, stored procedures, and database objects.
• Develop Python scripts for data extraction, transformation, and automation.
• Work in Linux environments to manage scripts, cron jobs, and system processes.
• Monitor and troubleshoot data pipeline failures.
• Ensure data integrity, consistency, and quality across systems.
• Collaborate with data analysts, software engineers, and business stakeholders.
• Optimize database performance and query execution.
• Participate in code reviews and follow best engineering practices.

Required Skills

• Strong proficiency in SQL (joins, subqueries, window functions, CTEs, indexing, query optimization).
• Good programming experience in Python.
• Hands-on experience with Linux commands and shell scripting.
• Understanding of ETL/ELT concepts and data warehousing.
• Knowledge of relational databases such as PostgreSQL, MySQL, Oracle, or SQL Server.
• Familiarity with Git for version control.
• Strong problem-solving and analytical skills.

Job Summary

We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate should have strong expertise in SQL, Python, Linux, and modern data engineering practices to support data integration, transformation, and analytics.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Write efficient and optimized SQL queries for data extraction, transformation, and reporting.
• Develop automation scripts using Python for data processing and workflow optimization.
• Work with Linux environments for deployment, monitoring, and troubleshooting.
• Ensure data quality, integrity, and reliability across data platforms.
• Collaborate with data analysts, software engineers, and business stakeholders to deliver data solutions.
• Monitor, troubleshoot, and optimize data pipelines for performance and scalability.
• Implement best practices for data security, governance, and documentation.

Required Skills

• Strong experience in Data Engineering concepts and ETL/ELT processes.
• Proficiency in SQL, including query optimization and database design.
• Strong programming skills in Python.
• Hands-on experience with Linux commands, shell scripting, and system administration basics.
• Experience with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Familiarity with Git/version control.
• Strong analytical and problem-solving skills.

Preferred Skills

• Experience with cloud platforms (AWS, Azure, or GCP).
• Knowledge of Apache Spark, Airflow, Kafka, or similar data engineering tools.
• Experience with data warehousing solutions and big data technologies.
• Understanding of CI/CD pipelines and containerization (Docker/Kubernetes).

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• Relevant certifications in cloud or data engineering are an added advantage.

Key Responsibilities

• Own and drive the product roadmap for data and AI-powered features end-to-end

• Translate complex data and AI capabilities into simple, intuitive product experiences for brands and creators

• Collaborate closely with data engineers, ML engineers, and business teams to define and deliver product requirements

• Define product metrics and success criteria — including data quality, model accuracy, and user engagement

• Identify gaps in existing data pipelines and work with engineering to build scalable solutions

• Conduct user research, competitor analysis, and market mapping to inform product decisions

• Prioritise features and manage the product backlog with a strong data-driven approach

• Work with the AI team to evaluate and integrate LLM and GenAI capabilities into the product

Must-Have Skills

• Total experience of 2–3 years with at least 1–2 years in data engineering or data analytics

• Transitioned into or actively pursuing a product management role

• Strong understanding of data pipelines, ETL processes, SQL, and data modelling

• Ability to write and interpret data queries to inform product decisions

• Excellent communication and stakeholder management skills

• Strong product thinking — ability to break down complex problems into simple product solutions

Company Name – Wissen Technology

Group of companies in India – Wissen Technology & Wissen Infotech

Work Location – Whitefield, Bangalore

While you may already know about Wissen and the company history, here is a quick rundown for you.

About Wissen Technology:

·    The Wissen Group was founded in the year 2000. Wissen Technology, a part of Wissen Group, was established in the year 2015.

·    Wissen Technology is a specialized technology company that delivers high-end consulting for organizations in the Banking & Finance, Telecom, and Healthcare domains. We help clients build world class products.

·    Our workforce has highly skilled professionals, with leadership and senior management executives who have graduated from Ivy League Universities like Wharton, MIT, IITs, IIMs, and NITs and with rich work experience in some of the biggest companies in the world.

·    Wissen Technology has grown its revenues by 400% in these five years without any external funding or investments.

·    Globally present with offices US, India, UK, Australia, Mexico, and Canada.

·    We offer an array of services including Application Development, Artificial Intelligence & Machine Learning, Big Data & Analytics, Visualization & Business Intelligence, Robotic Process Automation, Cloud, Mobility, Agile & DevOps, Quality Assurance & Test Automation.

·    Wissen Technology has been certified as a Great Place to Work®.

·    Wissen Technology has been voted as the Top 20 AI/ML vendor by CIO Insider in 2020.

·    Over the years, Wissen Group has successfully delivered $650 million worth of projects for more than 20 of the Fortune 500 companies.

·    We have served client across sectors like Banking, Telecom, Healthcare, Manufacturing, and Energy. They include likes of Morgan Stanley, Goldman Sachs, MSCI, StateStreet, Flipkart, Swiggy, Trafigura, GE to name a few.

Job Title: Azure Fabric Data Engineer / AI Engineer

Experience: 4–8 Years

Location: Pune(Hybrid)

Job Summary

We are seeking Azure Fabric Data Engineers with experience in data engineering, Power BI, and AI to build modern data platforms and AI-driven solutions on Microsoft Fabric.

Key Responsibilities

• Develop ETL/ELT pipelines using Microsoft Fabric.
• Integrate data from multiple enterprise systems into Fabric.
• Build and optimize Lakehouse and Data Warehouse solutions.
• Develop Power BI dashboards and reports.
• Build AI-powered applications, AI Agents, and chatbots using Azure AI Services and Azure OpenAI.
• Collaborate with business and technical teams to deliver scalable analytics solutions.

Required Skills

• Microsoft Fabric
• Data Engineering and ETL/ELT
• SQL, Python, PySpark
• Power BI
• Azure AI Services / Azure OpenAI
• Data Modeling
• Git and Azure DevOps

Preferred: Experience with Financial Services/Capital Markets, Generative AI, RAG, or LLM-based applications.

Data Engineer – Splunk & ELK Stack

Job Summary

We are seeking a skilled Data Engineer with hands-on experience in Splunk, the ELK Stack (Elasticsearch, Logstash, Kibana), and modern data engineering practices. The ideal candidate will design, build, and maintain scalable data pipelines, log analytics platforms, and monitoring solutions to support business intelligence, security, and operational excellence.

Key Responsibilities

• Design, develop, and maintain scalable data ingestion and ETL/ELT pipelines.
• Configure, administer, and optimize Splunk environments for log collection, indexing, searching, and reporting.
• Develop and maintain ELK Stack solutions using Elasticsearch, Logstash, Kibana, and Beats.
• Build dashboards, visualizations, alerts, and reports for infrastructure, application, and security monitoring.
• Integrate data from multiple structured and unstructured sources into centralized analytics platforms.
• Optimize Elasticsearch clusters for performance, scalability, and high availability.
• Troubleshoot data pipeline failures, indexing issues, and system performance bottlenecks.
• Automate deployment and configuration using scripting and Infrastructure as Code where applicable.
• Collaborate with DevOps, Security, Cloud, and Application teams to implement observability and monitoring solutions.
• Ensure data quality, governance, and compliance with organizational standards.
• Document technical designs, operational procedures, and best practices.

Required Skills

• Strong experience with Splunk Enterprise administration and development.
• Hands-on experience with the ELK Stack:
• Elasticsearch
• Logstash
• Kibana
• Beats (Filebeat, Metricbeat, Winlogbeat, etc.)
• Experience building ETL/ELT pipelines and data integration workflows.
• Strong SQL skills and experience with relational databases.
• Experience with Python, Shell scripting, or Java for automation.
• Understanding of log management, monitoring, and observability concepts.
• Experience working with Linux environments.
• Knowledge of REST APIs and data ingestion techniques.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with Git and CI/CD pipelines.

Preferred Skills

• Experience with Kafka, Spark, or other streaming technologies.
• Knowledge of Docker and Kubernetes.
• Experience with Terraform, Ansible, or other Infrastructure as Code tools.
• Understanding of SIEM concepts and security analytics.
• Experience with Prometheus, Grafana, or OpenTelemetry.
• Exposure to big data technologies and distributed systems.

We are looking for a hands-on Data Engineer to help build and manage our data platform for reporting, analytics, and future data science use cases.

The role will involve working with SQL, Python, PySpark, AWS, ETL/ELT pipelines, data warehouses, and BI/reporting tools. Our architecture may use technologies such as ClickHouse, Redshift, AWS Glue, Airflow, Step Functions, Lambda, S3, and CDC-based replication tools based on scale, cost, and operational needs.

Key Responsibilities

·     Design, build, and maintain scalable ETL/ELT data pipelines from multiple databases, applications, and external systems.

·     Build raw, cleaned, and business-ready data layers to support reporting, analytics, and future data science use cases.

·     Write efficient SQL, Python, and PySpark jobs for data ingestion, transformation, validation, and processing.

·     Implement workflow orchestration using Apache Airflow, AWS Glue, Step Functions, or similar tools.

·     Work with data warehouses such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

·     Support cross-service reporting as the architecture moves towards independent microservice databases.

·     Build reusable reporting tables, aggregates, summaries, and basic data marts.

·     Monitor, troubleshoot, and optimize data pipelines, warehouse queries, and processing jobs.

·     Implement data quality checks for freshness, completeness, consistency, duplicates, and reconciliation.

·     Support BI/reporting needs through tools such as Power BI, Metabase, Superset, Redash, or similar.

·     Apply data governance, access control, security, and PII-handling best practices.

·     Collaborate with engineering, DevOps, product, business, finance, risk, and support teams.

Required Skills

·     Strong expertise in SQL for joins, aggregations, window functions, query optimization, and analytical reporting.

·     Hands-on experience with Python for data processing, automation, validation, and scripting.

·     Working experience with PySpark / Apache Spark for processing large datasets.

·     Good understanding of ETL/ELT pipelines, data warehousing, and data modelling concepts.

·     Experience with workflow orchestration using Apache Airflow, AWS Step Functions, AWS Glue, or similar tools.

·     Experience with AWS data services such as S3, Glue, Lambda, Step Functions, Redshift, DMS, CloudWatch, or similar.

·     Experience with any data warehouse such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

·     Understanding of relational databases, preferably PostgreSQL.

·     Ability to debug data mismatches, failed pipelines, slow queries, and data quality issues.

·     Exposure to BI tools such as Power BI, Metabase, Superset, Redash, or similar.

Good ownership, problem-solving, communication, and collaboration skills.

Please find below the job description for Senior Azure Fabric Data Architect role with Wissen Technology.

Website and Company profile:

www.wissen.com

LinkedIn Page:

https://www.linkedin.com/company/wissen-technology/

Job Description:

Experience: 8–15+ Years

Location: Wissen Office (Pune/Bengaluru)

Position: 1

Job Summary

We are looking for an experienced Azure Fabric Data Architect to lead the design and implementation of an enterprise data platform on Microsoft Fabric. The role involves architecting scalable data solutions, defining data governance, and enabling AI-driven analytics for a global financial services client.

Key Responsibilities

• Design end-to-end data architecture using Microsoft Fabric.
• Build enterprise Lakehouse, Data Warehouse, and OneLake solutions.
• Define data ingestion, ETL/ELT, governance, security, and performance strategies.
• Lead architecture for AI-powered analytics, AI Agents, and enterprise chatbots using Azure AI services.
• Work with business stakeholders to translate requirements into technical solutions.
• Mentor engineering teams and provide technical leadership.

Required Skills

• Microsoft Fabric (Data Factory, Lakehouse, Data Warehouse, OneLake)
• Azure Data Engineering
• Power BI
• Azure AI Services / Azure OpenAI
• Data Architecture & Data Modeling
• SQL, Python
• Azure DevOps, CI/CD
• Strong stakeholder management and solution design experience

About Ritually

Ritually is building the definitive process discovery platform for back office work. Our product fuses underutilized system telemetry with computer vision to help large enterprise and scaling mid-market companies deeply understand and reimagine their highest value and most repetitive processes for a world where humans and agents work together. We're based in New York and Denver.

We believe deeply in trust (of our customers and each other), craft, customer obsession, and speed.

You'll be joining an AI-native, fast-moving, and repeat founding team. Ritually's founders previously built and exited a startup (Involvio) to Cisco. The company is funded and working with design partners.

The Role

This is a founding applied-AI role. You'll be building our core data pipeline and intelligence layer with the founding team from 0-1 You'll be tackling our largest technical challenges across technologies.

What You'll Do

• Design and build data pipelines that capture and turn high volumes of system activity into structured, queryable data.
• Turn raw activity streams into processes: sessionize event logs, cluster recurring sequences, and use LLMs to label and summarize what's happening.
• Build the evaluation backbone from scratch: stand up synthetic data generation pipelines that produce labeled scenarios to measure accuracy and catch regressions.
• Own data quality and privacy.
• Partner closely with the founders and the rest of engineering to ship features end to end.

What We're Looking For

• 1-4 years of experience in data engineering, AI/ML engineering, or backend work with a data focus (some of this can be project or research experience).
• Strong in Python and/or TypeScript, comfortable in SQL, and able to build data pipelines you can trust.
• Hands-on experience working with LLMs structured output, prompting, and wrangling non-determinism while keeping behavior reliable.
• A practical sense for evaluation: you know that "it looks right" isn't the same as "it's measurably right."
• Care about data privacy and handling sensitive information responsibly.
• Comfort with ambiguity and a real appetite to own a hard, open-ended problem.

Nice to Have

• Background in process mining, sequence / event-log analysis, or workflow analytics.
• Deeper PostgreSQL: window functions, partitioning, pg_cron, query performance.
• Embeddings and vector search (pgvector) or semantic retrieval.
• Familiarity with cloud infrastructure.
• Any prior early-stage startup experience.
• Degree in computer science or a related field.

Job Title : Data Engineer – Databricks

Experience : 6+ Years

Location : Noida / Hyderabad / Chennai / Pune / Bengaluru (Hybrid)

Shift : IST (Normal Shift)

Job Summary :

We are seeking an experienced Data Engineer with strong expertise in Databricks, Snowflake, Python, and Spark to build and optimize scalable data pipelines and support AI/ML model deployments. The ideal candidate should have experience working with cloud-based data platforms and preferably possess exposure to the Healthcare domain.

Required Skills :

• Databricks (Preferred)
• Snowflake
• Python
• Apache Spark
• SQL
• Azure Cloud
• Kubernetes
• Apache Airflow
• GitHub & CI/CD Pipelines
• AI/ML Model Deployment
• Data Analytics

Preferred :

• Experience in the Healthcare domain.
• Strong understanding of scalable data engineering architectures and best practices.

Role Overview

We are seeking a Senior SQL Developer & ETL Engineer with 5+ years of experience for a 100% remote opportunity.

Please Note: This is not a pure Data Engineering role. We are looking for a true SQL Specialist. Your core strength must lie in relational database development, schema design, and writing high-performance database logic with Python, ETL, Cloud. SQL mastery and database architecture are the absolute heart of this role.

If you are a database developer who loves diving into query execution plans, refactoring messy stored procedures for 10x performance, and building clean data models from scratch, this role is for you.

Key Responsibilities

1. Database Architecture & Schema Design

• Design, implement, and maintain robust relational database schemas.
• Architect optimal data models for both operational (OLTP) and analytical (OLAP/Data Warehousing) workloads.
• Implement Normalization (3NF) and dimensional modeling (Star/Snowflake schemas) as required.

2. Advanced Database Programmability

• Write, debug, and optimize highly complex Stored Procedures, Functions, Triggers, and Views to handle core business logic at the database level.
• Utilize advanced SQL techniques such as CTEs, Window Functions, and complex analytical queries to solve business problems.

3. Performance Tuning & Indexing

• Analyze query execution plans, identify performance bottlenecks, and implement advanced indexing strategies (B-Tree, Clustered/Non-Clustered, Partitioning).
• Refactor legacy SQL code and manage statistics, locking, and concurrency mechanisms to ensure sub-second response times.

4. Python & Cloud ETL/ELT Pipelines

• Develop, schedule, and maintain scalable data ingestion and transformation pipelines to connect disparate data sources.
• Leverage Cloud Data Platforms alongside modern Python libraries to build efficient data movement workflows.

5. Data Integrity & Governance

• Establish strict database constraints, data validation routines, and automated quality checks to guarantee absolute data accuracy.

Required Technical Skills

• Expert-Level SQL & DB Programmability (5+ Years): Mastery of writing server-side logic (Stored Procedures/Functions) and complex queries in enterprise platforms like PostgreSQL, SQL Server, Oracle, or MySQL.
• Advanced Database Optimization: Deep, under-the-hood understanding of database engines, execution plans, indexing strategies, and concurrency/locking control.
• Python for Data Engineering (3+ Years): Proficient in writing clean, modular Python scripts for API integration, data manipulation, and ETL processing (using libraries like Pandas, SQLAlchemy, or custom database connectors).
• Cloud Data Experience: Hands-on experience working with, migrating to, or developing within major cloud environments (AWS, Azure, GCP) and modern cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Data Modeling Methodologies: Practical experience designing Star/Snowflake schemas, handling Slowly Changing Dimensions (SCD), and balancing normalization vs. denormalization.

Remote & Soft Skills

• Legacy Refactoring Mindset: You genuinely enjoy opening up a massive, poorly optimized 500-line legacy stored procedure and refactoring it for maximum efficiency.
• Autonomous Execution: Proven ability to manage your own time, architecture tasks, and deliverables without micromanagement in a fully remote setup.
• Asynchronous Communication: Exceptional written and verbal English communication skills to collaborate seamlessly across time zones.

Nice-to-Haves

• Experience migrating legacy on-premise infrastructure and stored procedures to modern cloud data warehouses.
• Familiarity with workflow orchestration tools like Apache Airflow or Prefect.
• Hands-on experience with dbt (data build tool) for in-warehouse transformations.

About Marseer AI

Marseer AI (www.marseerai.com) is a modular AI activation platform built for DTC and retail e-commerce brands. damStack is Marseer AI's Snowflake-native, dbt-driven data and marketing activation product. It follows a Listen -> Reflect -> React architecture across composable applications, each running inside a customer's own Snowflake data warehouse.

Role Overview

We are looking for a Senior Data Engineer to join the damStack engineering team at Marseer AI. You will design and implement data pipelines, dbt transformation models, and Snowflake-native data products for retail and e-commerce brands. This is a hands-on, high-ownership role across ingestion, transformation, activation workflows, and client onboarding.

What You Will Do

- Design, build, and maintain dbt models across staging, intermediate, and mart layers for customer identity, segmentation, journey orchestration, and activation outputs.

- Implement incremental dbt models, snapshots, and tests to ensure data freshness, accuracy, and reliability.

- Contribute to damStack's Open Schema and unified customer_360 semantic layer.

- Build and refine SQL-based rule engines in Snowflake for priority resolution, frequency capping, and activation orchestration.

- Configure and manage Airbyte connectors for bidirectional data sync such as MongoDB to Snowflake and Snowflake to Klaviyo.

- Build and maintain Dagster pipelines to orchestrate dbt runs, Airbyte sync jobs, and cross-pipeline dependencies.

- Support integration of external marketing platforms into the damStack data layer.

- Work directly with client brands to understand data sources, schemas, and business requirements.

- Translate client data into damStack's standardized activity schema and entity resolution framework.

- Troubleshoot data quality and integration issues in client environments.

- Contribute to single-tenant Snowflake deployments, data quality tests, monitors, alerting, and technical design documentation.

Requirements

- 5+ years of professional experience in data engineering.

- Advanced proficiency in dbt Core or Cloud, including incremental models, snapshots, tests, macros, and multi-layer DAG design.

- Strong hands-on Snowflake experience, including DDL/DML, Snowflake Tasks, query optimization, and multi-tenant data architecture.

- Expert-level SQL, including window functions, CTEs, complex joins, and performance tuning.

- Ability to communicate pipeline designs and technical decisions clearly to technical and non-technical stakeholders.

Strongly Preferred

- Experience with Airbyte or comparable ELT / connector platforms.

- Familiarity with Dagster or similar orchestration tools such as Airflow or Prefect.

- Prior experience in a SaaS or data product company shipping reusable, multi-tenant data infrastructure.

- Understanding of identity resolution patterns, surrogate key architectures, customer data platforms, or experimentation / A/B testing data pipelines.

Good to Have

- Familiarity with Klaviyo or other marketing activation / ESP platforms.

- Experience with MongoDB or document-store integrations.

- Prior experience in retail or e-commerce data domains.

What We Are Looking For

- Availability for US business hours, with at least 5 hours of overlap with America/New_York.

- Ownership mindset, attention to schema naming, test coverage, documentation, and reliable pipelines.

- Collaborative, structured communication with a distributed team.

What We Offer

- Compensation range: INR 20-30 LPA.

- Fully remote role; work from anywhere in India, with Hyderabad-based candidates preferred.

- High-ownership engineering work on Snowflake, dbt, Airbyte, and Dagster.

- Direct exposure to real DTC and retail e-commerce data problems at scale.

- A small, senior team where your contributions are visible.

Join Hutech Solutions – Innovate, Lead, and Transform!

We are a global AI-driven software services and product engineering powerhouse, founded and led by

visionary technology leaders from Walmart. We are redefining the future of technology by building

next-gen solutions that empower businesses across Banking, Finance, eCommerce, and Logistics

industries.

At Hutech, we don’t just build software—we create impact. Our culture fosters innovation, creativity,

and continuous learning, enabling our team to push boundaries and solve real-world challenges using

cutting-edge AI tools and techniques.

Position Summary

We are looking for a skilled Data Engineer with strong expertise in Amazon Redshift, advanced SQL,

and data modelling to design, build, and optimize scalable data platforms on AWS. The ideal

candidate will play a key role in developing reliable data pipelines, enforcing data quality standards,

and enabling analytics and reporting across the organization.

Key Responsibilities

● Design, build, and optimize data models (fact/dimension, star/snowflake schemas) with a

strong focus on performance and scalability.

● Develop and maintain complex SQL queries in Amazon Redshift for analytics, reporting, and

downstream consumption.

● Optimize Redshift performance using distribution styles, sort keys, query tuning, and workload

management (WLM).

● Build and orchestrate scalable data pipelines using AWS Glue, Amazon EMR, Apache Spark,

and Airflow.

● Implement data quality checks, validation rules, and monitoring frameworks to ensure

accuracy and consistency.

● Work closely with analytics, BI, and business teams to translate requirements into robust data

solutions.

● Manage and optimize data storage and movement using Amazon S3.

● Ensure best practices in data security, governance, and documentation.

● Troubleshoot data issues and provide root-cause analysis and long-term fixes.

Required Qualifications

● Bachelor’s degree in Computer Science, Engineering, or a related field

● 3–5 years of hands-on experience in data engineering

● Strong advanced SQL skills (mandatory) with deep experience in Amazon Redshift

● Proven expertise in data modeling for analytical workloads

● Strong understanding of AWS data services, including: Amazon Redshift, AWS Glue, AmazonS3, Amazon EMR

● Experience with data pipeline and workflow orchestration tools such as Apache Airflow

● Hands-on experience with Apache Spark

● Proficiency in at least one programming/scripting language such as Python, Java, or Scala

● Solid understanding of data quality, validation, and best practices

Nice to have

● Experience designing large-scale analytics and reporting platforms

● Familiarity with BI tools and downstream analytics use cases

● Experience with cost optimization and performance tuning on AWS

● Exposure to CI/CD for data pipelines

Key Skills

● Amazon Redshift (SQL – Advanced)

● Data Modeling (Analytics-focused)

● AWS (S3, Glue, EMR)

● Apache Airflow

● Apache Spark

● Python / Java / Scala

● Data Quality & Optimization

Location: Bangalore (Hybrid/Onsite)

Experience: 5–8 Years

Work location -Manyata Tech park

Job Description

We are seeking a skilled GCP Data Engineer with 5–8 years of experience in designing, developing, and maintaining scalable data pipelines and cloud-based data solutions. The ideal candidate should have strong expertise in Google Cloud Platform (GCP), Python, PySpark, SQL, and Data Engineering concepts.

Key Responsibilities

• Design, build, and optimize scalable ETL/ELT data pipelines.
• Develop and maintain data processing solutions using Python and PySpark.
• Work with large-scale structured and unstructured datasets.
• Implement data ingestion, transformation, and data quality frameworks.
• Build and manage data solutions on Google Cloud Platform (GCP).
• Develop and optimize complex SQL queries, stored procedures, and data models.
• Collaborate with business stakeholders, data analysts, and cross-functional teams to understand data requirements.
• Monitor, troubleshoot, and improve data pipeline performance and reliability.
• Ensure data governance, security, and compliance standards are followed.
• Support data warehousing and analytics initiatives.

Required Skills

• 5–8 years of experience in Data Engineering.
• Strong programming experience in Python.
• Hands-on experience with PySpark and distributed data processing.
• Strong expertise in SQL and database performance tuning.
• Experience with Google Cloud Platform (GCP) services such as:
• BigQuery
• Cloud Storage
• Dataflow
• Dataproc
• Cloud Composer
• Pub/Sub
• Experience in designing ETL/ELT workflows.
• Knowledge of data warehousing concepts and dimensional modeling.
• Experience with version control tools such as Git.
• Strong problem-solving and analytical skills.

Preferred Skills

• Experience with CI/CD pipelines and DevOps practices.
• Exposure to orchestration tools like Airflow/Cloud Composer.
• Experience working in Agile/Scrum environments.
• Knowledge of streaming data processing and real-time data pipelines.

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

Role: Data Engineer

 Experience: 5+ Yrs

Type: Hybrid (3 Days a week)

Location: Chennai, Bangalore, Hyderabad, Pune, Kolkatta

End Client: Cognizant

Contract Duration: 6 Months

Shift:- 10 AM - 7 PM IST

Must Have: 

* Strong experience in Python

* Expertise in Data Engineering Frameworks

* Hands-on experience with ETL processes

* CI/CD

* Experience working on GCP (Google Cloud Platform)

GCP Data Engineer

Experience: 8 – 15 yrs

Grade: C2/D1

Skill: GCP + Python/Pyspark

NP: Immediate joiners

• 8+ years of hands-on experience in Python.
• 8+ years of hands-on experience in Data Engineering.
• 5+ Years of hands-on experience in GCP Big Query.
• Experience in building scalable data pipelines and automation frameworks.
• Experience migrating data and pipelines from SQL Server to GCP Big Query.
• Familiarity with CI/CD tools and Agile methodologies.
• Good understanding on Data Governance, Data Quality, Metadata, Lineage
• Expertise in Data Model design in Big query.
• Expertise in writing optimal Big query SQL and Stored Proc.
• Expertise in GCS Cloud Storage, Pub Sub, Cloud Composer, DAG, Apache Airflow, Data Flow, Data Proc, Data Plex, Cloud Run.
• Expertise in Vertex AI and Feature Store.
• Expertise in Spark and Apache Beam is desirable.

Email resumes to: naveenkb @ 10xscale.ai

Solid Fundamentals and exceptional problem-solving skills

Solid and fluent understanding of algorithm and data structures

Proficiency in Scala + Spark

Experience Range: 3 to 7 Years

Requirement Some or all of them – because we believe intelligent people can pick up whatever they need in a short period of time. You just need to prove that you can:

Excellent programming skills and knowledge of Java / Scala

Excellent software design, problem solving and debugging skills

Experience with modern Big data technologies such as Spark, NoSQL, Cassandra, Kafka, Map Reduce, Hadoop ecosystem is a must have

Experience with data analytics and ability to mine data to obtain insights is much appreciated

Scrum Master

Bangalore (Marathahalli)

Key Responsibilities

• Lead digital transformation initiatives for global clients within the data engineering domain, aligning technical solutions with business objectives
• Act as a bridge between business and technical teams to gather, analyze, and translate requirements into user stories and acceptance criteria
• Own and manage product backlogs, ensuring continuous grooming and prioritization aligned with client goals
• Facilitate Agile/Scrum ceremonies including daily stand-ups, sprint planning, and retrospectives
• Collaborate with cross-functional teams (Data Engineering, QA, DevOps, and stakeholders) to ensure seamless delivery
• Drive Agile best practices and continuous improvement in processes, timelines, and product quality
• Track and report team performance metrics, sprint progress, and release planning

Required Qualifications

• 8+ years of experience in IT services with a mix of business analysis and Agile delivery roles
• Strong experience working with data engineering teams / data platforms
• Proven experience in delivering client-facing digital products and managing complex stakeholder environments
• Hands-on experience with Agile tools such as JIRA, Confluence, and backlog management platforms
• Strong analytical, communication, and facilitation skills with the ability to balance business and technical priorities

Strong Microsoft Fabric / Azure Data Architect profile

Mandatory (Experience 1) – Must have 8+ years of experience in Data Architecture / Data Engineering, with strong exposure to enterprise-scale data platform modernization initiatives

Mandatory (Experience 2) – Must have 3+ years of deep hands-on experience in Microsoft Fabric ecosystem including Fabric Lakehouse, OneLake, and Data Factory, with large-scale implementations

Mandatory (Experience 3) – Strong expertise in designing and implementing Medallion (Bronze/Silver/Gold) architecture and scalable lakehouse platforms supporting batch and real-time workloads

Mandatory (Experience 4) – Strong experience in Azure data ecosystem including Azure Data Factory, Azure Synapse, ADLS, and Power BI, with good understanding of cloud-native data architectures

Mandatory (Experience 5) – Proven experience designing scalable data models including Dimensional Modelling (Star/Snowflake) and/or Data Vault for enterprise data warehouses

Mandatory (Experience 6) – Must have hands-on experience building ingestion pipelines including batch, streaming, and CDC pipelines using tools like Spark, Kafka, or Fabric pipelines

Mandatory (Experience 7) – Strong experience in implementing data governance frameworks including data cataloging, lineage, metadata management, and security controls

Mandatory (Skill 1) – Proven experience in building CI/CD pipelines for data platforms using Azure DevOps / Git, including automated deployment and environment management

Mandatory (Skill 2) – Hands-on experience designing AI/ML-ready data platforms, enabling advanced analytics, machine learning, and Generative AI use cases

Mandatory (Skill 3) – Experience with orchestration and workflow tools, and integrating data platforms with BI tools like Power BI for enterprise reporting

Mandatory (Note) – Only immediate joiners (within 15 days) will be considered

Experience: 6+ Years

Location: Chennai & Pune

Work Model: Hybrid

Notice Period: Immediate Joiners Preferred

Role Overview

We are looking for a highly skilled Senior Python Engineer to design, develop, and scale robust backend systems and data-driven applications. The ideal candidate should have strong problem-solving skills, experience with modern Python frameworks, and exposure to cloud and emerging technologies like Generative AI and LLMs.

Key Responsibilities

• Design, develop, and maintain scalable applications using Python
• Build and optimize RESTful APIs using Flask or FastAPI
• Work on data manipulation, processing, and transformation using Python libraries
• Collaborate with cross-functional teams to define and deliver high-quality solutions
• Develop efficient, reusable, and reliable code with strong attention to performance
• Implement containerization and orchestration using Docker and Kubernetes
• Ensure application security, data protection, and compliance best practices
• Manage code versioning using Git and follow CI/CD best practices
• Contribute to cloud-based deployments and infrastructure
• Explore and implement solutions using Generative AI and Large Language Models (LLMs)

Required Skills & Qualifications

• 6+ years of hands-on experience in Python development
• Strong understanding of Python fundamentals and problem-solving skills
• Experience with data manipulation libraries (e.g., Pandas, NumPy)
• Expertise in building REST APIs using Flask or FastAPI
• Solid understanding of ORM frameworks (e.g., SQLAlchemy, Django ORM)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Hands-on experience with Docker and Kubernetes
• Strong knowledge of application security principles
• Proficiency in Git and version control practices
• Exposure to Generative AI concepts and working with LLMs

About the role:

We are looking for a skilled Data Engineer with hands-on expertise in Dagster orchestration or GCP with Bigquery and Apache Airflow, modern data pipeline development, and architecture implementation. The ideal candidate will design, build, and optimize scalable data pipelines with strong SQL proficiency, data modelling expertise.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Dagster.

• Build and manage Dagster components such as: o Ops / Assets o Schedules o Sensors o Jobs o Resource definitions

• Implement and maintain Medallion Architecture (Bronze, Silver, Gold layers).

• Write optimized and production-grade SQL scripts for transformations and data validation.

• GCP, Big query, Apache Airflow – expertise is must if not familiar with Dagster and orchestration.

Must Have

• 4+ years of experience in Data Engineering.

• Strong hands-on experience with Dagster (optional) and workflow orchestration.

• Strong hands-on experience with GCP, Big query and Apache Airflow. • Solid understanding of data pipeline design patterns.

• Experience implementing Medallion Architecture.

• Advanced SQL skills (complex joins, CTEs, performance tuning).

• Experience working with GCP cloud data platform.

Why Join Us:

• Collaborative work environment.

• Exposure to modern tools and scalable application architectures.

• Medical cover for employee and eligible dependents.

• Tax beneficial salary structure.

• Comprehensive leave policy

• Competency development training programs.

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.

Foster a culture of technical excellence by mentoring and inspiring a team of Data analysts and engineers. Lead deep-dive code reviewa, prompte best-practice data modeling and ensure the squad adopts modern engineering standards like CI/CD For data

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.

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: Data Architect – AI/ML (Travel Domain)

We’re hiring a Data Architect to build and scale data systems powering AI/ML solutions in the travel domain. In this role, you will design data lakes/warehouses, create robust ETL pipelines, and enable real-time analytics for flight, hotel, and booking platforms. You will work closely with data scientists and engineering teams to support personalization, pricing, and recommendation engines.

Key Requirements:

• 5+ years in data architecture / engineering
• Strong experience with AWS/GCP/Azure and big data tools
• Expertise in ETL, data modeling, and pipeline design
• Good understanding of ML data workflows
• Experience in travel, e-commerce, or high-volume platforms is a plus

If you’re passionate about building scalable data ecosystems and driving AI-led innovation, we’d love to connect.

Job Title: Data Engineer

Location City: Gurugram

Industry: Research and Advisory Services

Role Overview

We are looking for a Senior Data Engineer (7–10 years) to play a foundational role in building Everest Group’s greenfield, Snowflake-based enterprise data platform.

This role is hands-on and ownership-driven, with a strong focus on:

Ingesting data from enterprise SaaS platforms Building scalable Snowflake ELT pipelines

Designing analytics-ready data models Owning the initial Snowflake platform foundations in collaboration with architecture leadership The ideal candidate has deep experience integrating CRM and marketing systems via APIs, is comfortable operating production- grade data pipelines, and can make sound decisions around performance, cost, and reliability.

Key Responsibilities

Robust Data Ingestion Pipelines From Enterprise SaaS Platforms, Including

 Salesforce (CRM)

 NetSuite (Finance)

 Marketing and RevOps tools such as Marketo, 6sense, Gong

 SharePoint (files, metadata, permissions)

Develop API-based Ingestion Frameworks Handling

 Authentication and authorization

 Pagination, rate limits, retries, and failures

 Incremental loads, soft deletes, and historical tracking

 Schema evolution and upstream source changes

ELT pipelines within Snowflake Write high-quality, optimized SQL for complex

transformations Build and manage data layers including raw, staged, and curated datasets

Optimize Snowflake warehouses, storage, and query performance with a strong focus on cost efficiency

Models Including

 Fact and dimension tables

 Star and snowflake schemas

 Slowly Changing Dimensions (SCD Type 1 and Type 2) Ensure data models support reporting, dashboards, and research analytics Partner with analytics and research teams to deliver analytics-ready, well documented datasets reliability including scheduling, monitoring, alerting, and recovery Implement data quality checks for accuracy, completeness, and freshness

 Support Snowflake Platform Foundations Including

 Warehouse and environment strategy (dev/test/prod)

 Role-based access control (RBAC) Secure handling of sensitive HR and finance data (PII) Troubleshoot and resolve data issues across ingestion, transformation, and consumption layers research, product, and technology stakeholders to translate business needs into data solutions Contribute to data platform architecture discussions and continuous improvement initiatives

Maintain clear documentation for pipelines, data models, and data flows

Follow modern engineering practices including Git-based version control and CI/CD workflows

Education And Experience

Bachelor’s or master’s degree in computer science, Information Technology, Engineering, Mathematics, or related field.

7–10 years of hands-on experience in data engineering or similar roles

Strong hands-on expertise with Snowflake, including ingestion, transformations, and performance optimization

Proven experience ingesting data from SaaS platforms via APIs (HR, CRM, or Marketing systems)

Advanced SQL skills and strong understanding of relational databases and data modeling

Strong Python experience for API integration, data ingestion, and automation Experience with cloud platforms (Azure preferred; AWS/GCP acceptable)

Experience with orchestration or transformation tools such as dbt, Azure Data Factory, or similar

Strong problem-solving skills, ownership mindset, and attention to detail

About Us: As India's fastest-growing D2C brand, we are at the forefront of innovation and transformation in the market. We’re a well-funded, rapidly growing (we have recently launched our 100th store), omnichannel D2C brand with a passionate and innovative team.

Job Summary: We are seeking a Data Engineer to help us design, build and maintain our BigQuery data warehouse by performing ETL operations and creating unified data models. You will work across various data sources to create a cohesive data infrastructure that supports our omnichannel D2C strategy.

Why Join Us: Experience the exciting world of India's billion-dollar D2C market. As a well-funded, rapidly growing omnichannel D2C brand, we are committed to changing the way India sleeps and sits. You'll have the opportunity to work with a passionate and innovative team and make a real impact on our success.

Key Responsibilities:

ETL Operations: Design, implement, and manage ETL processes to extract, transform, and load data from various sources into BigQuery.

Data Warehousing: Build and maintain a robust data warehouse in BigQuery, ensuring data integrity, security, and performance.

Data Modeling: Create and manage flat, unified data models using SQL and DBT to support business analytics and reporting needs.

Performance Optimization: Optimize ETL processes and data models to ensure timely data delivery for reporting and analytics.

Collaboration: Work closely with data analysts, product managers, and other stakeholders to understand data requirements and deliver actionable.

Documentation: Maintain comprehensive documentation of data workflows, ETL processes, and data models for reference and onboarding

Troubleshoot: Monitor and troubleshoot data pipeline issues, ensuring timely resolution to minimize disruption to business operations.

Skills and Qualifications:

1. Proficiency in SQL and experience with BigQuery
2. Minimum 2 years of experience in data engineering or a similar role
3. Experience with data pipeline and ETL tools (e.g., Apache Airflow, Talend, AWS Glue)
4. Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and their data services
5. Experience with data warehousing solutions (e.g., Amazon Redshift, Google BigQuery, Snowflake)
6. Knowledge of data modeling, data architecture, and data governance best practices.
7. Excellent problem-solving skills and attention to detail
8. Knowledge of DBT (Data Build Tool) for data transformation
9. Self-motivated, proactive, and highly accountable
10. Excellent communication skills to effectively convey technical concepts and solutions

Bonus point - Prior experience in E-commerce or D2C space

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’