Top Data Engineer Jobs in Chennai

Perform data migration/refresh from production, support Python-based data refresh framework, run post-migration fixes, validate and reconcile data, monitor and resolve batch jobs and pipeline issues, perform performance checks, and coordinate with dependent teams.

Join the Event Streaming and Data Replication platform as a Data Engineer to design, build, and operate cloud-native data platforms. Work with Airflow, Spark, Kubernetes, and Python; deploy containerized apps on AWS/Azure; use IaC (Terraform/CFT/ARM); implement CI/CD (GitHub, Artifactory, Jenkins); configure monitoring and alerts; and collaborate across teams to deliver highly available, scalable solutions.

Design, build, and maintain cloud data pipelines and architectures using SQL, Python, Snowflake, and AWS. Implement CI/CD for data workflows, use developer assistance tools, and collaborate across teams to deliver reliable data solutions.

Design, build, and maintain scalable ELT/data pipelines and connectors using Airflow, Python, and PySpark. Implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, collaborate with cross-functional teams, and document data processes and governance.

Build and operate a scalable data platform: design and maintain ETL/ELT pipelines, Bronze/Silver/Gold data layers, data models and semantic layers, manage migrations and schema changes, ensure data quality/observability, enable analytics and future event-driven/recommendation systems, and use AI-assisted workflows to accelerate engineering.

Lead and architect scalable cloud-based data platforms, build data ingestion/transformation code, mentor engineers, define best practices (CI/CD, testing, observability), drive data strategy, evaluate new tools, and enforce governance, security, and privacy.

Data Engineer responsibilities include designing and building scalable data management systems, optimizing data pipelines, managing data warehouses, and ensuring data quality and compliance.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Build and maintain scalable ETL pipelines on GCP, transform large/disconnected datasets, automate and optimize data delivery, support stakeholders, and apply data engineering best practices including testing and release management.

Design, build, and maintain scalable ETL/ELT pipelines and microservices for marketing and product data, ingesting from ad platforms (especially Meta Ads) and event systems. Enable campaign performance tracking, lead funnel analysis, attribution, and revenue reporting. Ensure data quality, observability, governance, and documentation while collaborating with marketing, growth, and analytics to deliver actionable datasets and drive business outcomes.

Design, build, and optimize Snowflake data models and dbt pipelines. Create Power BI reports and paginated RDLs with DAX and row-level security. Manage CI/CD with Azure DevOps, version control with Git, enforce data quality and governance, and optimize warehouse cost and performance while collaborating with business stakeholders.

Design, build, and maintain scalable ETL/ELT data pipelines on GCP/BigQuery using Python and Spark. Integrate systems (APIs, Oracle EBS), implement workflow orchestration, ensure data quality and security, monitor performance, and document processes while collaborating with analysts and business teams.

Data Engineer responsible for building and maintaining ETL pipelines, data processing workflows, and data platforms. Must have strong hands-on experience with Healthcare Revenue Cycle Management and understanding of healthcare data workflows. Role requires hybrid (onsite + offshore) work and immediate availability.

Build and prepare data pipelines: mapping, cleansing, transformation, integration, and wrangling using Python and SQL. Perform feature engineering for structured and unstructured data and support API and web app deployment for data products.

Design, develop, and maintain scalable cloud-based data pipelines and ETL/ELT processes. Build and optimize data solutions on GCP (BigQuery, Dataflow), ensure data quality, governance, monitoring, and high availability. Develop backend data processing services in Java, support BI, analytics, and ML teams, troubleshoot pipeline issues, and contribute to data platform architecture and modernization.

The Data Engineer will design and maintain data models, develop BI solutions, integrate data, and optimize data flows while collaborating with stakeholders.

Design, optimize, and manage scalable cloud databases (AWS RDS, BigQuery). Write efficient SQL, build Python data tools using OOP, support pipelines, monitor performance, ensure data integrity, security, and backups, and collaborate with data teams.

Design and implement data federation and lakehouse architectures (Starburst/Trino), build scalable ETL/ELT pipelines using Python and Spark, optimize Spark and federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, access control, and data masking for analytics and AI use.

As a Data Engineer, you will build scalable ETL/ELT pipelines, optimize data structures, and develop impactful Power BI solutions for stakeholders.

Design, develop and maintain data pipelines, optimize data architectures, and collaborate with data scientists while ensuring data quality and governance.

Design and optimize scalable data platforms using Microsoft Fabric and Databricks; create data pipelines and models for enterprise analytics while collaborating with data science teams.

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.

The IT Operations Engineer will manage AI and Data platforms, ensure operational health, lead incident resolution, and automate tasks while collaborating with engineering teams.

The Engineering Manager leads the Data Engineering team, overseeing the design of scalable data pipelines using AWS technologies, mentoring engineers, and collaborating with stakeholders on data-first initiatives.

Build and maintain SQL/dbt data models and BI layer (LookML/Looker) for the Insights product. Design transformations, dashboards, row-level security, and tests; translate business needs into end-to-end technical solutions; optimize complex queries and refactor models as the data platform modernizes. Collaborate with North America teams and use AI-assisted tools to accelerate development.