Top Data Engineer Jobs in Chennai

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.

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, build, and optimize end-to-end data solutions using Microsoft Fabric, Azure Synapse, Databricks, Azure Data Factory, and Power BI. Develop scalable real-time and batch pipelines, data models, semantic and dimensional models, dashboards, reports, and self-service analytics. Apply medallion architecture, DataOps, monitoring, security, governance, quality controls, and performance tuning. Integrate Azure Machine Learning and streaming technologies when needed while delivering enterprise data and BI solutions for clients.

Develop and deploy statistical and machine learning models for fraud detection, credit risk scoring, portfolio management, pricing, and related financial applications. Collaborate with data engineering, DevOps, MLOps, and business stakeholders to capture data, understand domain requirements, build production ML pipelines, and optimize model training throughput and scoring response times. Support model development, testing, monitoring, and deployment at scale.

Designs, builds, and maintains scalable data pipelines, ETL processes, data architectures, and integrations. Ensures data quality, governance, security, compliance, and accessibility while monitoring performance and troubleshooting issues. Collaborates with data scientists, analysts, and engineers to define requirements and deliver reliable solutions. Participates in code reviews, documentation, data modeling, schema design, and CI/CD implementation for data workflows.

Design and build scalable cloud-native data pipelines to ingest, clean, transform, chunk, embed, index, and serve enterprise data for LLM applications. Optimize vector database architectures, embedding generation, and RAG pipelines, collaborate with ML and software teams, implement Airflow orchestration, and ensure pipeline performance, monitoring, and reliability on AWS.

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.

Designs and maintains SAP-integrated ETL/ELT pipelines connecting ECC, S/4HANA, BW/4HANA, and cloud data platforms. Responsibilities include SAP data extraction, transformation, real-time integrations, dimensional modeling, database optimization, data quality monitoring, alerting, cost optimization, and documentation. The role partners with functional consultants, BI teams, FP&A stakeholders, and business leaders while developing cloud skills across AWS services such as Glue, Lambda, Airflow, and Redshift.

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.

Ten-week hybrid data engineering internship in Houston supporting meaningful business projects. The intern will receive mentorship, training, and professional development while gaining exposure to data and analytics models, architecture, data platform design, data ingestion, visualization, and potentially AI/ML. Candidates should be pursuing a relevant bachelor's degree, demonstrate strong analytical and communication skills, and have SQL and Python experience.

Design, build, and maintain Azure-based data infrastructure and pipelines. Implement data models and schemas, ensure data quality, optimize distributed storage and processing (Databricks/PySpark), and establish engineering best practices while partnering with cross-functional teams.

Develop, maintain, and enhance BI solutions and data warehouses using SQL Server, SSIS/SSAS/SSRS and Microsoft Fabric. Write and run unit tests, debug complex BI systems, capture requirements, implement DW design (dimensional modeling), collaborate cross-functionally, and mentor junior data engineers to improve BI processes.

Designs, develops, deploys, and operates scalable enterprise data products, pipelines, semantic models, and curated datasets. Builds batch, streaming, event-driven, ETL/ELT, and cloud-native data solutions using platforms such as Snowflake and Databricks. Ensures data quality, governance, lineage, security, observability, scalability, and cost efficiency while supporting analytics, AI/ML, operational processes, and regulatory requirements. Partners with business, product, architecture, governance, and platform teams and contributes to CI/CD, DataOps, documentation, and Agile delivery.

Designs and refines data platform blueprints and architectures, integrating systems and data models with Integration and Data Architects. Leads platform performance and scalability efforts, resolves complex issues, coordinates cross-functional initiatives, and mentors team members. Serves as a subject matter expert, contributes to team decisions, and requires strong Red Hat OpenShift expertise, with AWS Administration experience preferred.

Supports the design, deployment, integration, troubleshooting, and optimization of scalable data platforms. Collaborates with integration and data architects to align platform components with systems, data models, and business requirements. The role also documents best practices, contributes to continuous improvement, participates in team discussions, and independently resolves technical issues while developing subject-matter expertise.

Leads the architecture, design, and delivery of enterprise data products and domain data assets. Establishes engineering standards, frameworks, governance, reliability, observability, and security practices. Guides cloud and data platform decisions, translates business priorities into technical roadmaps, mentors engineers, supports hiring, and drives modernization, cost optimization, and technical debt reduction. Builds trusted data products for AI, machine learning, and advanced analytics.

Design and refine the organization's data platform blueprint, collaborate with architects, lead and mentor teams, build scalable data pipelines, optimize platform performance, and resolve complex data integration challenges to ensure reliable data flow and accessibility.

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, develop, and maintain scalable ETL and ELT pipelines, data architectures, data lakes, warehouses, and lakehouse solutions. Implement data quality, validation, monitoring, and lineage frameworks. Build and optimize cloud data solutions using Spark, Kafka, and Databricks, while tuning SQL queries and pipeline workflows for performance and cost efficiency.

Designs and refines the data platform architecture, builds scalable data pipelines, ensures system integration and performance, leads team decisions, mentors staff, and coordinates stakeholders to align platform capabilities with business needs.

Design, build, and maintain batch and real-time data pipelines; collaborate with architects, product owners, data scientists and analysts to provision and integrate data; ensure pipeline performance, quality, and scalability; enforce engineering best practices (CI/CD, testing, monitoring); mentor other engineers and support cross-team data integrations.

Design, build, and maintain data pipelines and ETL processes; ensure data quality and availability; collaborate with cross-functional teams to support analytics and reporting; implement data solutions and support data platform operations.

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.

Lead the design and implementation of ETL processes, develop Python solutions, and create data visualizations, collaborating with cross-functional teams.