50+ Data engineering Jobs in Bangalore (Bengaluru) | Data engineering Job openings in Bangalore (Bengaluru)

About the Role

We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.

Key Responsibilities

• Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
• Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
• Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
• Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
• Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
• Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

• Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
• Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
• Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
• Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
• Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
• Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
• Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
• Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
• Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

• Hands-on exposure to Microsoft Fabric for data integration and analytics.
• Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
• Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
• Domain expertise in financial services.

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.

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

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

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.

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.

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.

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

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’

About the Role: 

We are looking for a Data Architect with a strong background in data engineering & cloud data platforms. The ideal candidate will design and implement scalable data architectures that power enterprise analytics, AI/ML, and GenAI solutions — ensuring data availability, quality, and governance across the organization. 

Key Responsibilities: 

Data Architecture & Strategy 

• Design & Architecture: Design and implement robust, scalable, and optimized data engineering solutions on the Databricks platform. Architect data pipelines that scale efficiently and reliably. 
• Data Pipeline Development: Develop ETL/ELT pipelines leveraging Databricks notebooks, Delta Lake, Snowflake tech stack, Azure Data Factory etc.  
• Cloud Integration: Work closely with cloud platforms like Azure, AWS, or GCP to integrate Databricks or Snowflake with data storage (e.g., ADLS, S3, etc.), databases, and other services. 
• Performance Optimization: Optimize the performance of data workflows by tuning Databricks clusters, improving query performance, and identifying bottlenecks in data processing. 
• Collaboration: Collaborate with data scientists, analysts, and business stakeholders to understand business requirements and translate them into scalable data solutions. 
• Data Governance & Security: Ensure best practices for data security, governance, and compliance when working with sensitive or large datasets. 
• Automation & Monitoring: Automate data pipeline deployments and create monitoring dashboards for ongoing performance checks. 
• Continuous Improvement: Stay up to date with the latest Databricks features and Snowflake eco system best practices to continuously improve existing systems and processes. 

Required Skills & Experience: 

• 12+ years of experience in Data Architecture / Data Engineering roles.  
• Proven expertise in data modeling, ETL/ELT design, and cloud-based data solutions (AWS Redshift, Snowflake, BigQuery, or Synapse). 
• Hands-on experience with data pipeline orchestration tools (Airflow, DBT, Azure Data Factory, etc.). 
• Proficiency in Python, SQL, and Spark for data processing and integration. 
• Experience with API integrations and data APIs for AI systems. 
• Excellent communication and stakeholder management skills. 

Role & Responsibilities:

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

Responsibilities:

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

Ideal Candidate:

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

NOTE:

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

Note-“Urgently Hiring – Immediate Joiners Preferred”

Data Engineering

Role & Responsibilities

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

Responsibilities:

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

Ideal Candidate

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

.

Strong Data Engineer Profile

Mandatory (Experience 1) – Must have 6+ years of hands-on experience in Data Engineering, with strong ownership of end-to-end data pipeline development.

Mandatory (Experience 2) – Must have strong experience in ETL/ELT pipeline design, transformation logic, and data workflow orchestration.

Mandatory (Experience 3) – Must have hands-on experience with any one of the following: Dataform, dbt, or BigQuery, with practical exposure to data transformation, modeling, or cloud data warehousing.

Mandatory (Experience 4) – Must have working experience on any cloud platform: GCP (preferred), AWS, or Azure, including object storage (GCS, S3, ADLS).

Mandatory (Core Skill 1) – Must have strong SQL skills with experience in writing complex queries and optimizing performance.

Mandatory (Core Skill 2) – Must have programming experience in Python and/or SQL for data processing.

Mandatory (Core Skill 3) – Must have experience in building and maintaining scalable data pipelines and troubleshooting data issues.

Job Description:

We are seeking a Cloud & AI Platform Engineer to design and operate AI-native infrastructure that supports large-scale machine learning, generative AI, and agentic AI systems. 

This role will focus on building secure, scalable, and automated multi-cloud platforms across AWS, Azure, GCP, and hybrid on-prem environments, enabling teams to deploy LLMs, AI agents, and data-driven applications reliably in production. 

You will work at the intersection of cloud engineering, MLOps, LLMOps, DevOps, and data infrastructure, helping build platforms that support RAG pipelines, vector search, AI model lifecycle management, and AI observability. 

Key Responsibilities

AI & Agentic Infrastructure 

• Design infrastructure to support agentic AI systems, autonomous agents, and multi-agent workflows. 
• Build scalable runtime environments for LLM orchestration frameworks. 
• Enable deployment of AI copilots, assistants, and autonomous decision systems. 

Common frameworks may include: 

• LangChain 
• LlamaIndex 
• AutoGPT 

LLMOps & AI Model Lifecycle 

Design and manage LLMOps pipelines for the full lifecycle of large language models: 

• Model deployment 
• Prompt management 
• Versioning 
• Evaluation and testing 
• Model monitoring 

Integrate with AI platforms such as: 

• Azure Machine Learning 
• Amazon SageMaker 
• Vertex AI 

Retrieval-Augmented Generation (RAG) Infrastructure 

Design and optimize RAG pipelines that integrate enterprise knowledge with LLMs. 

Responsibilities include: 

• Document ingestion pipelines 
• Embedding generation workflows 
• Knowledge indexing 
• Query orchestration 
• Retrieval optimization 
• Support scalable semantic search architectures. 

Vector Database & Knowledge Infrastructure 

Deploy and manage vector databases used for AI applications and semantic retrieval. 

Common technologies include: 

• Pinecone 
• Weaviate 
• Milvus 
• FAISS 

Responsibilities include: 

• Index optimization 
• Query latency tuning 
• Scalable embedding storage 
• Hybrid search architecture 

Multi-Cloud AI Infrastructure 

Design and maintain AI-ready infrastructure across: 

• Amazon Web Services 
• Microsoft Azure 
• Google Cloud Platform 

Key responsibilities include: 

• GPU infrastructure management 
• Distributed training environments 
• Hybrid cloud integrations with on-prem data centers 
• Infrastructure scaling for AI workloads 

Data Platforms & Integration 

• Support deployment and optimization of data lakes, data warehouses, and streaming platforms. 
• Work with data engineering teams to ensure secure and scalable data infrastructure. 

Cloud Architecture & Infrastructure 

• Design and implement scalable multi-cloud infrastructure across Azure, AWS, and Google Cloud. 
• Build hybrid cloud architectures integrating on-premise environments with cloud platforms. 
• Implement high availability, disaster recovery, and auto-scaling architectures for AI workloads. 

DevOps, Platform Engineering & Automation 

Build automated cloud infrastructure using modern DevOps practices. 

Tools may include: 

• Terraform 
• Docker 
• Kubernetes 
• GitHub Actions 

Responsibilities include: 

• Infrastructure as Code (IaC) 
• Automated deployments 
• CI/CD pipelines for AI models and services 
• Platform reliability and scalability 

AI Observability & Monitoring 

Implement observability frameworks to monitor AI systems in production. 

This includes: 

• Model performance monitoring 
• Prompt evaluation 
• Hallucination detection 
• Latency and throughput analysis 
• Cost monitoring for LLM usage 

Tools may include: 

• Arize AI 
• WhyLabs 
• Weights & Biases 

Security, Governance & Responsible AI 

Ensure AI systems follow strong governance and security practices. 

Responsibilities include: 

• Data privacy and compliance 
• Model governance frameworks 
• Secure model deployment 
• Monitoring model bias and drift 
• AI risk management 

Support enterprise frameworks for Responsible AI and AI compliance. 

Data & Security 

• Experience with data lake architectures, distributed storage, and ETL pipelines 
• Knowledge of data security, encryption, IAM, and compliance frameworks 
• Familiarity with AI governance and responsible AI practices 

Required Skills 

Cloud & Infrastructure 

• Strong experience in Azure (must have), AWS or GCP 
• Hybrid and multi-cloud architecture 
• GPU infrastructure management 

DevOps & Automation 

• Kubernetes 
• Docker 
• Terraform 
• CI/CD pipelines 

AI / ML Platforms 

• MLOps pipelines 
• Model deployment 
• Model monitoring 

AI Application Infrastructure 

• Vector databases 
• RAG pipelines 
• LLM orchestration frameworks 

Programming 

Experience in one or more languages: 

• Python 
• Go 
• Java 
• TypeScript 

Preferred Qualifications 

• Experience building AI copilots or autonomous agents 
• Knowledge of distributed model training - Knowledge of GPU infrastructure and distributed training 
• Familiarity with AI evaluation frameworks - Familiarity with model monitoring, drift detection, and AI observability 
• Experience building enterprise AI platforms 

Education & Experience 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field 
• 4–8+ years experience in cloud infrastructure, DevOps, or platform engineering 
• Experience working in data-driven or AI-focused environments 

What Success Looks Like 

• Reliable ML model deployment pipelines - Reliable infrastructure for LLMs and AI agents, Scalable RAG knowledge platforms 
• Efficient multi-cloud infrastructure management - Fast deployment cycles for AI products 
• Secure and scalable AI-ready cloud platforms 
• Strong automation and governance across cloud and AI systems 

Immediate hiring for Senior Data Engineer

📍 Location: Hyderabad/Bangalore

💼 Experience: 7+Years

🕒 Employment Type: Full-Time

🏢 Work Mode: Hybrid

📅 Notice Period: 0-1Month serving notice only

   We are seeking a highly skilled and motivated Data Engineer to join our innovative team. As a Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support our enterprise-wide data-driven initiatives. You will collaborate closely with cross-functional teams to ensure the availability, reliability, and performance of our data systems and solutions.

🔎 Key Responsibilities:

• Data Pipeline Development
• Data Modeling and Architecture
• Data Integration and API Development
• Data Infrastructure Management
• Collaboration and Documentation

🎯 Required Skills:

• Bachelor’s degree in computer science, Engineering, Information Systems, or a related field.
• 7+ years of proven experience in data engineering, software development, or related technical roles.
• 7+ years of experience in programming languages commonly used in data engineering (Python, Java, SQL, Stored Procedures, Scala, etc.).
• 7+ years of experience with database systems, data modeling, and advanced SQL.
• 7+ years of experience with ETL tools such as SSIS, Snowflake, Databricks, Azure Data Factory, Stored Procedures, etc.
• Experience with big data technologies such as Hadoop, Spark, Kafka, etc.
• 5+ years of experience working with cloud platforms like Azure, AWS, or Google Cloud.
• Strong analytical, problem-solving, and debugging skills with high attention to detail.
• Excellent communication and collaboration skills in a team-oriented, fast-paced environment.
• Ability to adapt to rapidly evolving technologies and business requirements.

Data Engineer MS Data Engineer + Snowflake/databrics Required Skills: · 6 to 8 years of being a practitioner in data engineering or a related field. Should have experience in Snowflake or Databricks. Experience with data processing frameworks like Apache Spark or Hadoop. Experience working on Databricks. Familiarity with cloud platforms (AWS, Azure) and their data services. Experience with data warehousing concepts and technologies. Experience with message queues and streaming platforms (e.g., Kafka). Excellent communication and collaboration skills. Ability to work independently and as part of a geographically distributed team. 

Hi,

We are seeking a senior data leader with deep functional expertise in Salesforce Sales and Service domains to own the enterprise data model, metrics, and analytical outcomes supporting Sales, Service, and Customer Operations.

This role is business‑first and data‑centric. The successful candidate understands how Salesforce Sales Cloud and Service Cloud data is generated, evolves over time, and is consumed by business teams, and ensures analytics accurately reflect operational reality.

Snowflake serves as the enterprise analytics platform, but Salesforce domain mastery and functional data expertise are the primary requirements for success in this role.

Core Responsibilities

Salesforce Sales & Service Data Ownership

·                 Act as the data owner and architect for Salesforce Sales and Service domains.

• Own Sales data including leads, accounts, opportunities, pipeline, bookings, revenue, forecasting, and CPQ (if applicable).
• Own Service data including cases, case lifecycle, SLAs, backlog, escalations, and service performance metrics.
• Define and govern enterprise‑wide KPI and metric definitions across Sales and Service.
• Ensure alignment between Salesforce operational definitions and analytics/reporting outputs.
• Own cross‑functional metrics spanning Sales, Service, and the customer lifecycle (e.g., customer health, renewals, churn).

Business‑Driven Data Modeling

·                 Design Salesforce‑centric analytical data models that accurately reflect Sales and Service processes.

• Model sales stage progression, pipeline history, and forecast changes over time.
• Model service case lifecycle, SLA compliance, backlog aging, and resolution metrics.
• Handle Salesforce‑specific complexities such as slowly changing dimensions (ownership, territory, account hierarchies).
• Ensure data models support operational dashboards, executive reporting, and advanced analytics.

Analytics Enablement & Business Partnership

·                 Partner closely with Sales Operations, Service Operations, Revenue Operations, Finance, and Analytics teams.

• Translate business questions into trusted, reusable analytical datasets.
• Identify data quality issues or Salesforce process gaps impacting reporting and drive remediation.
• Enable self‑service analytics through well‑documented, certified data products.

Technical Responsibilities (Enabling Focus)

·                 Architect and govern Salesforce data ingestion and modeling on Snowflake.

• Guide ELT/ETL strategies for Salesforce objects such as Opportunities, Accounts, Activities, Cases, and Entitlements.
• Ensure reconciliation and auditability between Salesforce, Finance, and analytics layers.
• Define data access, security, and governance aligned with Salesforce usage patterns.
• Partner with data engineering teams on scalability, performance, and cost efficiency.

Required Experience & Skills

Salesforce Sales & Service Domain Expertise (Must‑Have)

·                 Extensive hands‑on experience working with Salesforce Sales Cloud and Service Cloud data.

• Strong understanding of sales pipeline management, forecasting, and revenue reporting.
• Strong understanding of service case workflows, SLAs, backlog management, and service performance measurement.
• Experience working directly with Sales Operations and Service Operations teams.
• Ability to identify when Salesforce configuration or process issues cause reporting inconsistencies.

Data & Analytics Expertise

·                 10+ years working with business‑critical analytical data.

• Proven experience defining KPIs, metrics, and semantic models for Sales and Service domains.
• Strong SQL and analytical skills to validate business logic and data outcomes.
• Experience supporting BI and analytics platforms such as Tableau, Power BI, or MicroStrategy.

Platform Experience 

·                 Experience using Snowflake as an enterprise analytics platform.

• Understanding of modern ELT/ETL and cloud data architecture concepts.
• Familiarity with data governance, lineage, and access control best practices.

Leadership & Collaboration

·                 Acts as a bridge between business stakeholders and technical teams.

• Comfortable challenging requirements using business and data context.
• Mentors engineers and analysts on Salesforce data nuances and business meaning.
• Strong communicator able to explain complex Salesforce data behavior to non‑technical leaders.

Thanks,

Ampera Talent Team

Role & Responsibilities

You will be responsible for architecting, implementing, and optimizing Dremio-based data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

Ideal Candidate

• Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

Preferred:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
• Exposure to Snowflake, Databricks, or BigQuery environments.
• Experience in high-tech, manufacturing, or enterprise data modernization programs.

Review Criteria:

• Strong Dremio / Lakehouse Data Architect profile
• 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio
• Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems
• Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts
• Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)
• Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics
• Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices
• Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline
• Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

Preferred:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) or data catalogs (Collibra, Alation, Purview); familiarity with Snowflake, Databricks, or BigQuery environments

Role & Responsibilities:

You will be responsible for architecting, implementing, and optimizing Dremio-based data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

Ideal Candidate:

• Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

Preferred:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
• Exposure to Snowflake, Databricks, or BigQuery environments.
• Experience in high-tech, manufacturing, or enterprise data modernization programs.

10+ years experience in data engineering or backend engineering

Should have a product based company experience

2+ years in a technical leadership or team-lead role

Expert-level experience with Kafka for high-throughput streaming systems

Strong hands-on expertise with PySpark for distributed data processing

Advanced experience with AWS Glue for ETL orchestration and metadata management

Proven experience building and upgrading real-time data lakes at scale

Hands-on knowledge of data warehouses such as Redshift or Snowflake

Experience with AWS services including S3, Kinesis, Lambda, and RDS

Job Title : Senior Software Engineer (Full Stack — AI/ML & Data Applications)

Experience : 5 to 10 Years

Location : Bengaluru, India

Employment Type : Full-Time | Onsite

Role Overview :

We are seeking a Senior Full Stack Software Engineer with strong technical leadership and hands-on expertise in AI/ML, data-centric applications, and scalable full-stack architectures.

In this role, you will design and implement complex applications integrating ML/AI models, lead full-cycle development, and mentor engineering teams.

Mandatory Skills :

Full Stack Development (React/Angular/Vue + Node.js/Python/Java), Data Engineering (Spark/Kafka/ETL), ML/AI Model Integration (TensorFlow/PyTorch/scikit-learn), Cloud & DevOps (AWS/GCP/Azure, Docker, Kubernetes, CI/CD), SQL/NoSQL Databases (PostgreSQL/MongoDB).

Key Responsibilities :

• Architect, design, and develop scalable full-stack applications for data and AI-driven products.
• Build and optimize data ingestion, processing, and pipeline frameworks for large datasets.
• Deploy, integrate, and scale ML/AI models in production environments.
• Drive system design, architecture discussions, and API/interface standards.
• Ensure engineering best practices across code quality, testing, performance, and security.
• Mentor and guide junior developers through reviews and technical decision-making.
• Collaborate cross-functionally with product, design, and data teams to align solutions with business needs.
• Monitor, diagnose, and optimize performance issues across the application stack.
• Maintain comprehensive technical documentation for scalability and knowledge-sharing.

Required Skills & Experience :

• Education : B.E./B.Tech/M.E./M.Tech in Computer Science, Data Science, or equivalent fields.
• Experience : 5+ years in software development with at least 2+ years in a senior or lead role.
• Full Stack Proficiency :
• Front-end : React / Angular / Vue.js
• Back-end : Node.js / Python / Java
• Data Engineering : Experience with data frameworks such as Apache Spark, Kafka, and ETL pipeline development.
• AI/ML Expertise : Practical exposure to TensorFlow, PyTorch, or scikit-learn and deploying ML models at scale.
• Databases : Strong knowledge of SQL & NoSQL systems (PostgreSQL, MongoDB) and warehousing tools (Snowflake, BigQuery).
• Cloud & DevOps : Working knowledge of AWS, GCP, or Azure; containerization & orchestration (Docker, Kubernetes); CI/CD; MLflow/SageMaker is a plus.
• Visualization : Familiarity with modern data visualization tools (D3.js, Tableau, Power BI).

Soft Skills :

• Excellent communication and cross-functional collaboration skills.
• Strong analytical mindset with structured problem-solving ability.
• Self-driven with ownership mentality and adaptability in fast-paced environments.

Preferred Qualifications (Bonus) :

• Experience deploying distributed, large-scale ML or data-driven platforms.
• Understanding of data governance, privacy, and security compliance.
• Exposure to domain-driven data/AI use cases in fintech, healthcare, retail, or e-commerce.
• Experience working in Agile environments (Scrum/Kanban).
• Active open-source contributions or a strong GitHub technical portfolio.

Skills: Gen AI, Machine learning Models, AWS/ Azure, redshift, Python, Apachi, Airflow, Devops, minimum 4-5years experience as Architect, should be from Data Engineering background.

• 8+ years of experience in data engineering, data science, or architecture roles.

• Experience designing enterprise-grade AI platforms.

• Certification in major cloud platforms (AWS/Azure/GCP).

• Experience with governance tooling (Collibra, Alation) and lineage systems

• Strong hands-on background in data engineering, analytics, or data science.

• Expertise in building data platforms using:

o Cloud: AWS (Glue, S3, Redshift), Azure (Data Factory, Synapse), GCP (BigQuery,

Dataflow).

o Compute: Spark, Databricks, Flink.

o Data modelling: dimensional, relational, NoSQL, graph.

• Proficiency with Python, SQL, and data pipeline orchestration tools.

• Understanding of ML frameworks and tools: TensorFlow, PyTorch, Scikit-learn, MLflow, etc.

• Experience implementing MLOps, model deployment, monitoring, logging, and versioning.

Criteria

Mandatory

Strong Dremio / Lakehouse Data Architect profile

Mandatory (Experience 1) – 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio

Mandatory (Experience 2) – Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems

Mandatory (Technical Skills 1) – Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts

Mandatory (Technical Skills 2) – Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)

Mandatory (Architecture) – Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics

Mandatory (Governance) – Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices

Mandatory (Stakeholder Management) – Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline

Mandatory (Company) – Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

Preferred

Criteria

Mandatory

Strong Dremio / Lakehouse Data Architect profile

Mandatory (Experience 1) – 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio

Mandatory (Experience 2) – Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems

Mandatory (Technical Skills 1) – Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts

Mandatory (Technical Skills 2) – Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)

Mandatory (Architecture) – Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics

Mandatory (Governance) – Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices

Mandatory (Stakeholder Management) – Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline

Mandatory (Company) – Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

ROLES AND RESPONSIBILITIES:

You will be responsible for architecting, implementing, and optimizing Dremio-based data Lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

IDEAL CANDIDATE:

• Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

PREFERRED:

• Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) and data catalogs (Collibra, Alation, Purview).
• Exposure to Snowflake, Databricks, or BigQuery environments.
• Experience in high-tech, manufacturing, or enterprise data modernization programs.

Criteria

Mandatory

Strong Dremio / Lakehouse Data Architect profile

Mandatory (Experience 1) – 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio

Mandatory (Experience 2) – Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems

Mandatory (Technical Skills 1) – Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts

Mandatory (Technical Skills 2) – Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)

Mandatory (Architecture) – Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics

Mandatory (Governance) – Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices

Mandatory (Stakeholder Management) – Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline

Mandatory (Company) – Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

10+ years experience in data engineering or backend engineering

2+ years in a technical leadership or team-lead role

Expert-level experience with Kafka for high-throughput streaming systems

Strong hands-on expertise with PySpark for distributed data processing

Advanced experience with AWS Glue for ETL orchestration and metadata management

Proven experience building and upgrading real-time data lakes at scale

Hands-on knowledge of data warehouses such as Redshift or Snowflake

Experience with AWS services including S3, Kinesis, Lambda, and RDS

Job Description – Senior Technical Business Analyst

Location: Trivandrum (Preferred) | Open to any location in India

Shift Timings - 8 hours window between the 7:30 PM IST - 4:30 AM IST

About the Role

We are seeking highly motivated and analytically strong Senior Technical Business Analysts who can work seamlessly with business and technology stakeholders to convert a one-line problem statement into a well-defined project or opportunity. This role is ideal for fresh graduates who have a strong foundation in data analytics, data engineering, data visualization, and data science, along with a strong drive to learn, collaborate, and grow in a dynamic, fast-paced environment.

As a Technical Business Analyst, you will be responsible for translating complex business challenges into actionable user stories, analytical models, and executable tasks in Jira. You will work across the entire data lifecycle—from understanding business context to delivering insights, solutions, and measurable outcomes.

Key Responsibilities

Business & Analytical Responsibilities

• Partner with business teams to understand one-line problem statements and translate them into detailed business requirements, opportunities, and project scope.
• Conduct exploratory data analysis (EDA) to uncover trends, patterns, and business insights.
• Create documentation including Business Requirement Documents (BRDs), user stories, process flows, and analytical models.
• Break down business needs into concise, actionable, and development-ready user stories in Jira.

Data & Technical Responsibilities

• Collaborate with data engineering teams to design, review, and validate data pipelines, data models, and ETL/ELT workflows.
• Build dashboards, reports, and data visualizations using leading BI tools to communicate insights effectively.
• Apply foundational data science concepts such as statistical analysis, predictive modeling, and machine learning fundamentals.
• Validate and ensure data quality, consistency, and accuracy across datasets and systems.

Collaboration & Execution

• Work closely with product, engineering, BI, and operations teams to support the end-to-end delivery of analytical solutions.
• Assist in development, testing, and rollout of data-driven solutions.
• Present findings, insights, and recommendations clearly and confidently to both technical and non-technical stakeholders.

Required Skillsets

Core Technical Skills

• 6+ years of Technical Business Analyst experience within an overall professional experience of 8+ years
• Data Analytics: SQL, descriptive analytics, business problem framing.
• Data Engineering (Foundational): Understanding of data warehousing, ETL/ELT processes, cloud data platforms (AWS/GCP/Azure preferred).
• Data Visualization: Experience with Power BI, Tableau, or equivalent tools.
• Data Science (Basic/Intermediate): Python/R, statistical methods, fundamentals of ML algorithms.

Soft Skills

• Strong analytical thinking and structured problem-solving capability.
• Ability to convert business problems into clear technical requirements.
• Excellent communication, documentation, and presentation skills.
• High curiosity, adaptability, and eagerness to learn new tools and techniques.

Educational Qualifications

• BE/B.Tech or equivalent in:
• Computer Science / IT
• Data Science

What We Look For

• Demonstrated passion for data and analytics through projects and certifications.
• Strong commitment to continuous learning and innovation.
• Ability to work both independently and in collaborative team environments.
• Passion for solving business problems using data-driven approaches.
• Proven ability (or aptitude) to convert a one-line business problem into a structured project or opportunity.

Why Join Us?

• Exposure to modern data platforms, analytics tools, and AI technologies.
• A culture that promotes innovation, ownership, and continuous learning.
• Supportive environment to build a strong career in data and analytics.

Skills: Data Analytics, Business Analysis, Sql

Must-Haves

Technical Business Analyst (6+ years), SQL, Data Visualization (Power BI, Tableau), Data Engineering (ETL/ELT, cloud platforms), Python/R

Review Criteria

• Strong Dremio / Lakehouse Data Architect profile
• 5+ years of experience in Data Architecture / Data Engineering, with minimum 3+ years hands-on in Dremio
• Strong expertise in SQL optimization, data modeling, query performance tuning, and designing analytical schemas for large-scale systems
• Deep experience with cloud object storage (S3 / ADLS / GCS) and file formats such as Parquet, Delta, Iceberg along with distributed query planning concepts
• Hands-on experience integrating data via APIs, JDBC, Delta/Parquet, object storage, and coordinating with data engineering pipelines (Airflow, DBT, Kafka, Spark, etc.)
• Proven experience designing and implementing lakehouse architecture including ingestion, curation, semantic modeling, reflections/caching optimization, and enabling governed analytics
• Strong understanding of data governance, lineage, RBAC-based access control, and enterprise security best practices
• Excellent communication skills with ability to work closely with BI, data science, and engineering teams; strong documentation discipline
• Candidates must come from enterprise data modernization, cloud-native, or analytics-driven companies

Preferred

• Preferred (Nice-to-have) – Experience integrating Dremio with BI tools (Tableau, Power BI, Looker) or data catalogs (Collibra, Alation, Purview); familiarity with Snowflake, Databricks, or BigQuery environments

Job Specific Criteria

• CV Attachment is mandatory
• How many years of experience you have with Dremio?
• Which is your preferred job location (Mumbai / Bengaluru / Hyderabad / Gurgaon)?
• Are you okay with 3 Days WFO?
• Virtual Interview requires video to be on, are you okay with it?

Role & Responsibilities

You will be responsible for architecting, implementing, and optimizing Dremio-based data lakehouse environments integrated with cloud storage, BI, and data engineering ecosystems. The role requires a strong balance of architecture design, data modeling, query optimization, and governance enablement in large-scale analytical environments.

• Design and implement Dremio lakehouse architecture on cloud (AWS/Azure/Snowflake/Databricks ecosystem).
• Define data ingestion, curation, and semantic modeling strategies to support analytics and AI workloads.
• Optimize Dremio reflections, caching, and query performance for diverse data consumption patterns.
• Collaborate with data engineering teams to integrate data sources via APIs, JDBC, Delta/Parquet, and object storage layers (S3/ADLS).
• Establish best practices for data security, lineage, and access control aligned with enterprise governance policies.
• Support self-service analytics by enabling governed data products and semantic layers.
• Develop reusable design patterns, documentation, and standards for Dremio deployment, monitoring, and scaling.
• Work closely with BI and data science teams to ensure fast, reliable, and well-modeled access to enterprise data.

Ideal Candidate

• Bachelor’s or master’s in computer science, Information Systems, or related field.
• 5+ years in data architecture and engineering, with 3+ years in Dremio or modern lakehouse platforms.
• Strong expertise in SQL optimization, data modeling, and performance tuning within Dremio or similar query engines (Presto, Trino, Athena).
• Hands-on experience with cloud storage (S3, ADLS, GCS), Parquet/Delta/Iceberg formats, and distributed query planning.
• Knowledge of data integration tools and pipelines (Airflow, DBT, Kafka, Spark, etc.).
• Familiarity with enterprise data governance, metadata management, and role-based access control (RBAC).
• Excellent problem-solving, documentation, and stakeholder communication skills.

Role: Azure Fabric Data Engineer

Experience: 5–10 Years

Location: Pune/Bangalore

Employment Type: Full-Time

About the Role

We are looking for an experienced Azure Data Engineer with strong expertise in Microsoft Fabric and Power BI to build scalable data pipelines, Lakehouse architectures, and enterprise analytics solutions on the Azure cloud.

Key Responsibilities

• Design & build data pipelines using Microsoft Fabric (Pipelines, Dataflows Gen2, Notebooks).
• Develop and optimize Lakehouse / Data Lake / Delta Lake architectures.
• Build ETL/ELT workflows using Fabric, Azure Data Factory, or Synapse.
• Create and optimize Power BI datasets, data models, and DAX calculations.
• Implement semantic models, incremental refresh, and Direct Lake/DirectQuery.
• Work with Azure services: ADLS Gen2, Azure SQL, Synapse, Event Hub, Functions, Databricks.
• Build dimensional models (Star/Snowflake) and support BI teams.
• Ensure data governance & security using Purview, RBAC, and AAD.

Required Skills

• Strong hands-on experience with Microsoft Fabric (Lakehouse, Pipelines, Dataflows, Notebooks).
• Expertise in Power BI (DAX, modeling, Dataflows, optimized datasets).
• Deep knowledge of Azure Data Engineering stack (ADF, ADLS, Synapse, SQL).
• Strong SQL, Python/PySpark skills.
• Experience in Delta Lake, Medallion architecture, and data quality frameworks.

Nice to Have

• Azure Certifications (DP-203, PL-300, Fabric Analytics Engineer).
• Experience with CI/CD (Azure DevOps/GitHub).
• Databricks experience (preferred).

Note: One Technical round is mandatory to be taken F2F from either Pune or Bangalore office