Top Data Engineer Jobs in Chennai

Design and implement AI/LLM-driven data extraction and ETL pipelines, build web crawling and API integrations, implement RAG systems, mitigate LLM hallucinations, evaluate third-party tools, and collaborate with content and analytics teams to deliver high-quality, scalable data for downstream analysis.

The Senior Data Engineer - II will design data pipelines, optimize ETL workflows, manage databases, and support Power BI reporting for business decisions.

The Technology Engineer will design, implement, and optimize DevOps and data platform solutions, focusing on CI/CD, containerization, big data, and distributed systems, while ensuring security and compliance.

The Data Visualization Developer will create and support dashboards in Power BI, develop SQL scripts, perform regression testing, and troubleshoot existing scripts.

As a Senior Data Engineer, you will develop and maintain data pipelines for healthcare data, ensuring data quality, reliability, and adherence to enterprise standards while collaborating with architects and stakeholders.

Lead the modeling of the Customer Data Platform, defining event taxonomy and ensuring reliable datasets for analytics, product insights, and machine learning. Collaborate with various teams to maintain data consistency and shape the architecture of the behavioral data platform.

The Senior Data Engineer will design and maintain pipelines for event data ingestion and validation, ensuring operational reliability and consistency for analytics.

The Senior Data Engineer will develop data ingestion, transformation, and enrichment pipelines, ensuring data quality and reliability. Responsibilities include creating Bronze and Silver data layers, using SQL and dbt for implementation, and collaborating with architects and stakeholders to align data solutions with enterprise standards.

Lead design, development, and maintenance of an ETL-based reporting framework to collect utilization metrics, automate billing/chargebacks, and build dashboards. Implement scalable PySpark/Databricks pipelines on Hadoop ecosystem, manage storage across Oracle, MongoDB, and Snowflake, schedule jobs with Autosys/Airflow, optimize performance, mentor team members, lead stakeholder requirements, coordinate QA and resolve production incidents.

Design, build, and maintain ETL/ELT pipelines using Informatica IICS and Snowflake. Implement data ingestion from structured and semi-structured sources, optimize Snowflake performance, ensure data quality and compliance for healthcare/life sciences datasets, collaborate with stakeholders, and support production deployments with CI/CD and troubleshooting.

Seeking a Senior Data Engineer to design and maintain data pipelines for the Risk & Compliance domain using PySpark and Python, ensuring data integrity and security while collaborating with cross-functional teams.

The role involves leading data science initiatives, mentoring team members, deploying models, and ensuring high-quality technical solutions. Responsibilities include coding, architecture design, and client requirement translation.

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.

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

The Data Platform Engineer will design and maintain data platforms, ensuring reliability and efficiency while collaborating with cross-functional teams to fulfill product and analytics needs.

The Principal Developer will lead the design, development, and maintenance of application and data integrations using various tools, ensuring high-quality solutions and collaboration across teams.

Lead customer-facing data engineering engagements: design and implement scalable batch and real-time data pipelines and platforms (Databricks, Delta Lake, ClickHouse, Azure). Own solution delivery, optimize performance and costs, build data products for analytics and AI, drive governance and reliability, collaborate across product and customer success, and mentor junior engineers.

Support and optimize large-scale Spark/YARN jobs and storage (S3/EMR). Build job-profiling, cost-analysis, AI-assisted automation, monitoring/alerting (Prometheus, Flink/Spark), and internal ops tooling to improve stability and reduce costs.

Support and validate data migration activities for legacy-to-modern core banking projects: maintain mapping and migration trackers, perform data validation and reconciliations, manage exception logs, coordinate SIT/UAT test data readiness, produce status dashboards, and support cutover/mock runs while ensuring data security and audit artifacts.

The Senior Data Engineer will design, develop, and optimize scalable batch and streaming data pipelines using Databricks, Snowflake, and modern data engineering techniques while ensuring data quality and collaborating with teams.

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

As a Senior Data Platform Engineer, you'll build and scale OpenGov's data infrastructure, lead ELT/ETL implementation, and ensure data governance, security, and observability while collaborating across teams.

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 and build high-throughput reconciliation systems and data pipelines. Implement real-time (Flink) and batch (Hive) workflows, write complex SQL for validation and anomaly detection, collaborate with Finance/Risk/Product on reconciliation rules, optimize for low latency, and leverage AI/LLM tooling to automate triage and improve operations.

Build and maintain large-scale PySpark/Spark data reconciliation pipelines on Hadoop/Big Data platforms; integrate with Kafka, APIs and microservices; support capital markets regulatory reporting and reconciliations; lead globally distributed teams, manage stakeholders, and deliver solutions with strong application design and optional UI integration.