Top Data Engineer Jobs in Chennai

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Leads the design, development, optimization, and support of ETL/ELT pipelines, data warehouses, data models, and cloud-based data solutions. Writes advanced SQL, implements testing and CI/CD practices, monitors production systems, resolves incidents, and improves performance and cost efficiency. Partners with analysts, data scientists, and stakeholders to deliver reliable datasets while documenting solutions and mentoring associate engineers.

Lead the design, development, and scaling of a digital experience analytics platform. Build data foundations and AI-driven decision systems using Azure, Databricks, dbt, Python, SQL, and cloud data technologies. Guide junior engineers, collaborate with product and analytics teams, support data architecture, modeling, governance, quality, and operationalization, and coordinate cloud deployments and technical delivery across onsite and offshore teams.

Build, monitor, optimize, and maintain cloud-based ETL/ELT pipelines supporting business intelligence and reporting. Develop high-performance SQL transformations, manage data warehouse schemas, tune query and pipeline performance, and optimize cloud costs. Collaborate with analysts, data scientists, actuaries, and architects to translate insurance and SaaS requirements into scalable technical solutions. Participate in code reviews, documentation, troubleshooting, and data integrity initiatives.

Design, develop, and maintain scalable Microsoft Fabric and Azure data solutions. Build Lakehouse, Data Warehouse, OneLake, ETL/ELT pipelines, data transformations, and integrations using Python, PySpark, SQL, and Fabric tools. Implement medallion architecture, optimize data models and workloads, establish data quality monitoring, and support CI/CD deployments. Collaborate with analytics and business teams to deliver enterprise-scale data platforms.

Designs and maintains Oracle Fusion Data Intelligence and OTBI reporting solutions. Collaborates with stakeholders to gather requirements and build dashboards, reports, data pipelines, and integrations across HCM, ERP, CRM, and external systems. Applies SQL, PL/SQL, data modeling, ETL, and data warehousing practices to ensure accurate, reliable analytics and reporting.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Designs, builds, and optimizes scalable batch and real-time data pipelines on GCP using Dataflow, Apache Beam, Java, and BigQuery. Integrates diverse data sources, improves pipeline performance and reliability, supports data governance and security, troubleshoots production issues, and collaborates with architects, analysts, DevOps, and business stakeholders.

Design and maintain analytical pipelines and reporting solutions using BigQuery and MariaDB. Develop Python and FastAPI tooling, APIs, and automation; write advanced SQL; apply AI/ML capabilities; document data architecture and lineage; and translate complex analysis into actionable business insights. Collaborate across departments on process improvements and independently own analytics projects end to end.

Design, build, and maintain scalable data pipelines and ETL/ELT workflows using Azure Data Factory, Databricks, PySpark, Python, and SQL. Transform large datasets, optimize data ingestion and queries, monitor pipeline performance, resolve data quality issues, support data modeling, document engineering standards, and contribute to cloud data infrastructure improvements. The role also involves collaboration with architects and senior engineers and requires familiarity with Azure Synapse, Delta Lake, governance, and security.

Lead backend and data-platform architecture while remaining hands-on with Java production code. Design and operate distributed systems for event ingestion, analytical querying, data processing, and low-latency serving. Work with ClickHouse, BigQuery, Bigtable, Kafka, Redis, GCP, and Dataflow to solve scalability, reliability, performance, and data-correctness challenges. Set technical direction, review designs, mentor engineers, and guide platform convergence across Wingify and AB Tasty.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include architecture, data modeling, versioning, synchronization, language editing and validation, debugging, automated testing, and platform quality. Collaborate with international engineering teams and domain experts to build a reliable, extensible development environment.

Develop a Java-based desktop IDE, coordination server, and relational data backend for a semiconductor test engineering DSL. Responsibilities include desktop UI development, data modeling, client-server synchronization, language editing and validation, architecture, debugging, automated testing, and platform quality. The role involves shaping a modular development environment and collaborating with international engineering teams and domain experts.

Senior data engineer responsible for designing, building, and optimizing scalable cloud-based data pipelines and lakehouse solutions on AWS. Lead complex integrations, implement data modeling and governance, promote engineering best practices, automate infrastructure and CI/CD, mentor engineers, and collaborate with stakeholders to deliver reliable analytics and BI solutions.

Build and support fund accounting data solutions for asset management, including integrations, transformations, reconciliations, controls, and finance reporting products. Develop large-scale data pipelines using Databricks, Python, PySpark, and SQL; work with investment data, NAV calculations, accruals, corporate actions, fees, and general ledger information. Support cloud-based delivery, data quality, orchestration, CI/CD, and direct collaboration with US stakeholders.

Design, build, modernize, and optimize ETL/ELT pipelines and Salesforce integrations across enterprise platforms. Migrate Informatica workflows to Azure Data Factory, develop advanced SQL transformations, and implement scalable data models, warehousing, governance, quality, and lineage frameworks. Own production monitoring, incident management, troubleshooting, orchestration, and documentation. Collaborate with Salesforce, architecture, DevOps, AI, and business teams to support analytics, automation, and AI use cases.

Design, build, optimize, and maintain scalable ETL/ELT pipelines and complex SQL transformations. Translate business needs into reusable data solutions, automate workflows with orchestration tools, and ensure reliability through testing, validation, monitoring, and documentation. Collaborate with analysts, data scientists, and stakeholders to deliver trusted datasets. Troubleshoot production issues, improve performance and cost efficiency, and follow coding, version control, and peer review practices.

Design, develop, optimize, and maintain scalable ETL pipelines using PySpark and AWS Glue. Build data ingestion and transformation workflows, orchestrate processes with AWS Step Functions, and develop Python-based AWS Lambda functions. Support AWS data lake architectures, analytical use cases, monitoring, reliability, and performance optimization. The role also involves SQL and may include Java microservices, REST APIs, and backend integrations.

Leads data engineering initiatives involving ETL/ELT pipelines, database optimization, orchestration, big data technologies, data modeling, BI performance, and data quality monitoring. Requires strong Scala, Python, SQL, and shell scripting skills, plus payments-domain expertise covering transaction processing, settlements, disputes, reconciliation, gateways, acquiring, issuing, and PCI-DSS compliance. The role also involves cross-functional communication, planning, facilitation, negotiation, and collaboration with senior stakeholders and clients.

The Senior Azure Data Engineer will design, develop, and maintain data infrastructure and pipelines, ensuring data quality, integrity, and alignment with business objectives. Responsibilities include collaborating with teams and establishing best practices for data engineering.

Design, build, and maintain enterprise ETL/ELT pipelines in Microsoft Fabric; ingest and transform data from diverse sources into dimensional star schemas; optimize performance and cost; implement data quality checks and monitoring; document architecture and pipelines; collaborate with analysts, stakeholders, and global engineering teams to deliver Power BI-ready analytics datasets.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Design, develop, and maintain data platforms and ETL processes on cloud-based big data systems. Own architecture, write code and APIs, build automation and CI/CD pipelines, collaborate with product managers, perform code reviews, and support data integration, modeling, and BI solutions for global customers.