Top Data Engineer Jobs in Chennai

Design and implement data quality processes, profile and cleanse data, deploy cloud-based data quality solutions (Azure/AWS/GCP), document issues and remediation, report metrics, and collaborate with data teams and stakeholders.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and Cloudera platform performance, integrate HDFS/Hive/Impala/HBase/Kafka, ensure data integrity/security, and implement version control and CI/CD for production pipelines.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Build and maintain scalable ETL/ELT pipelines and data platforms, optimize queries and databases, design analytics-ready data models, ensure data quality and governance, collaborate with stakeholders, and support data products and automation for business intelligence and analytics.

Design, build, and support ETL workflows to migrate and transform legacy EHR data into athenahealth systems. Develop Python utilities, validate and reconcile data, support HL7/FHIR interoperability and EHI export tasks, troubleshoot issues, maintain documentation, and collaborate across product, sales, and operations to improve migration processes and automation.

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.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

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

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, 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, 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.

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.

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.

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 and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Support electrical design, engineering, commissioning, and delivery of liquid-cooled data centre solutions: prepare layouts, SLDs, wiring diagrams, BOMs, perform load calculations, assist testing/FAT/SAT, troubleshoot commissioning issues, coordinate cross-functional teams, and ensure compliance with IEC/NEC/IEEE and company HSE standards.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

As a Data Engineer, you'll build and monitor large-scale data pipelines, develop the DBT setup for data transformation and solve customer problems using your SQL skills.

Manage and support AKS-based enterprise data platform infrastructure, administer Apache NiFi, Azure Event Hub and Functions, implement CI/CD with Azure DevOps, automate infrastructure with Terraform/Bicep/ARM, monitor with Azure Monitor and Log Analytics, troubleshoot production incidents, ensure security, capacity planning, and collaborate with data engineering teams.

Lead the architecture and implementation of a distributed data platform for microscopy, ensuring scalability and high-performance in data management.

As a key member of the Data Infrastructure team, you will develop and optimize software features for Apache Hudi's transactional engine, enhance database performance, and solve complex data processing challenges.