Top Data Engineer Jobs in Chennai

Design, develop, and maintain Power BI dashboards, reports, semantic models and datasets. Gather business requirements, optimize DAX/Power Query transformations, and modernize reporting using Microsoft Fabric and Power Platform. Administer Fabric and Power BI environments (capacities, gateways, workspaces, security, deployment pipelines, governance). Promote self-service analytics and support Fabric adoption across business and IT stakeholders.

Leads a team of data engineers and owns the architecture, implementation, governance, security, and optimization of a GCP Data Lakehouse platform. Responsibilities include designing Spark and Delta Lake pipelines, managing Unity Catalog, improving SQL and ETL performance, enabling analytics and machine learning use cases, implementing CI/CD with GitLab and Jenkins, and communicating technical strategies to stakeholders.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Build, operate, and scale ingestion pipelines for marketing data (ad platforms, ecommerce, analytics). Normalize and transform data into shared schemas, ensure multi-tenant reliability, observability, and security, and partner with AI/data-science teams to expose clean data for agents.

Designs and operates scalable data and machine learning platforms using Databricks, Python, SQL, Spark, and PySpark. Builds batch, streaming, and production ML pipelines; manages Delta Lake, Unity Catalog, Workflows, MLflow, governance, data quality, observability, and model deployment. Partners with data scientists and business teams, optimizes performance and costs, contributes to platform architecture, mentors engineers, and documents operational practices.

Senior technology leader responsible for strategy, architecture, and delivery of enterprise-scale GenAI/AI agent platforms and Python-based data ecosystems. Leads ~15+ engineer agile teams, drives AI PDLC, regulatory-compliant data pipelines (PySpark/Databricks/Kafka), microservices and cloud-native deployments, and ensures model governance, explainability, and stakeholder alignment across Retail and Wealth Risk.

Design and build enterprise Salesforce solutions across Sales, Service and Experience Clouds using Apex, LWC and Visualforce. Implement AI-driven features with Einstein, Einstein GPT and Data Cloud, integrate external ML services, create prompt-driven Flows, implement Platform Events and CI/CD, maintain/security-govern solutions, write bulkified unit tests, and support field service integrations (Dispatch.me).

Design, build, and maintain batch and real-time ETL/ELT data pipelines and workflows. Automate ingestion, aggregation and logging; ensure data accuracy, security, and compliance. Implement data warehouse and lake solutions, mentor junior engineers, and collaborate with stakeholders to meet business requirements.

Design and maintain scalable ETL/ELT pipelines, optimize data processes, collaborate with teams for analytics, and ensure data integrity.

Design, build, test, and operate cloud-based data ingestion and ETL systems. Develop backend services in Golang (with Python where appropriate), run workloads on Kubernetes/AWS, troubleshoot distributed systems, and collaborate to improve scalability, reliability, and performance.

Design, build and maintain cloud-oriented big data solutions for ingestion, processing, cleaning and exposition. Improve data quality, implement ETL and data-warehouse architectures, and collaborate with DDS/Data Foundation teams to ensure platform reliability.

Senior Java engineer responsible for building data lake and big data solutions (preferably on Spark) with cloud experience (Azure/AWS/GCP), REST services, OOA/OOD and JEE design patterns, PostgreSQL expertise, TDD practices, and working in Agile teams. Optional: front-end (Angular/React), Docker/Kubernetes, investment banking domain knowledge.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from relational, NoSQL, file, and streaming sources. Ensure data quality, implement error handling, and collaborate with analysts, scientists, and architects to deliver performant data solutions.

Design, configure, and support backend software and APIs; manage configuration files and environments; test and document API endpoints; collaborate on system data flow; participate in large-scale live data migrations (billions of records) into FinCloud; produce documentation and troubleshooting guides; work in agile teams and support integration and observability efforts.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Design, develop and maintain data ingestion and transformation pipelines, optimize data architectures, manage data sources, and collaborate with teams on data analytics and ML datasets.

Lead the design and development of data pipelines and architectures, ensuring data quality and governance while mentoring junior engineers.

The Senior Data Engineer will design data ingestion pipelines from SAP to Snowflake, optimize ELT processes, ensure data quality, and collaborate with stakeholders for analytics solutions.

Build and scale an AI-powered stock research platform: design data pipelines ingesting SEC and market data, develop backend APIs, implement semantic search with embeddings and vector DBs, improve production reliability and performance, and own features end-to-end from design through deployment and monitoring.

Design, build, and deploy enterprise-scale Agentic AI and multi-agent systems, LLM fine-tuning and RAG pipelines, vector search, AI copilots, and production AI microservices. Lead model development, MLOps/CI-CD, cloud integration (Azure/AWS/GCP), monitoring, governance, and deliver measurable automation and business outcomes.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

Responsible for guiding teams in utilizing technology effectively, mentoring colleagues and devising advanced solutions to meet client challenges.

As a Senior Engineer in Data Analytics, you'll work on challenging problems using cutting-edge technology alongside talented colleagues to drive innovative solutions.

The Principal Engineer, Data Science role involves leading data science projects and providing strategic insights using extensive IT experience.

The Senior Staff Engineer, Data Science role involves guiding team direction, solving complex challenges for clients, and leveraging technical expertise to innovate solutions.