Top Data Engineer Jobs in Chennai

Design, implement, and administer SAP Analytics Cloud (SAC) planning and reporting solutions. Lead SAC modelling, story creation, SAC admin, and migrations from BPC to SAC. Collaborate with data, product, and functional teams to define data acquisitions, virtual models in HANA, pipelines, and security. Provide support, training, and stabilization efforts while managing multiple projects and mentoring team members.

Design, develop, and maintain data solutions, pipelines, data models, and ETL processes. Ensure data quality, reliability, and efficient data migration across systems. Collaborate with multiple teams, provide technical solutions, make team decisions, and serve as a subject matter expert. Monitor and troubleshoot pipeline performance while supporting analytical and operational data needs.

Build scalable Snowflake data pipelines and ETL/ELT processes across projects. Ensure data quality, security, performance, and support structured and semi-structured data for analytics. Collaborate with architects, engineers, business teams, and Team Leads to translate requirements into solutions. Contribute to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and GenAI initiatives using Snowflake Cortex capabilities.

Design, develop, and maintain data solutions, pipelines, and ETL processes across systems. Ensure data quality, monitor and optimize pipeline performance, gather data requirements, and collaborate with stakeholders. The role requires independent execution, team participation, and contribution as a data engineering subject matter expert.

Design, develop, and maintain data solutions and ETL pipelines using Snowflake and related tools. Ensure data quality, lead cross-functional data strategy, mentor junior engineers, resolve complex data issues, and manage team decisions for large-scale data migrations and deployments.

Build scalable Snowflake data pipelines and ETL/ELT processes for structured and semi-structured data. Ensure data quality, security, and performance through advanced SQL, Snowpipe, Streams, Tasks, and stored procedures. Collaborate with architects, engineers, and business teams to translate requirements into solutions, support analytics and reporting, contribute to CoE guidelines and frameworks, and participate in proof-of-concept initiatives.

Build scalable, secure Snowflake data pipelines and ETL/ELT processes across projects. Develop solutions using structured and semi-structured data, optimize Snowflake performance, write advanced SQL, ensure data quality and security, and collaborate with architects, engineers, and business teams. The role also contributes to CoE guidelines, frameworks, reference architectures, proof-of-concepts, and delivery-team support while tracking emerging Snowflake and GenAI capabilities.

Design and lead scalable Snowflake data architectures across ingestion, storage, transformation, and access. Establish data modeling standards, security practices, performance tuning, and architecture guidelines. Mentor teams, lead proofs of concept, contribute to centers of excellence, and support presales through proposals, estimations, solutioning, and client presentations. Collaborate with integration and data architects while evaluating Snowflake and generative AI capabilities.

Design, build, and optimize cloud-native data pipelines and platforms on GCP. Develop ETL/ELT workflows with SQL and Python, implement transformations with dbt, orchestrate jobs with Airflow, provision infrastructure via Terraform, maintain CI/CD, ensure data quality/governance, and support analytics, ML, and production stability.

Build, maintain, test, and optimize scalable data architectures and pipelines. Extract, transform, and load data for analysts and scientists; source and integrate new data; improve data quality, security, governance, reliability, and performance. Collaborate with technology and architecture teams, practice DevOps, troubleshoot issues, manage platform costs, and contribute to strategic data solutions.

Build, maintain, test, and optimize scalable data architectures and pipelines. Extract, transform, and load data into platforms for analysts and scientists, integrate new data sources, improve data quality and performance, perform root cause analysis, and support DevOps practices. Collaborate with technology, architecture, and business stakeholders to develop cost-effective, secure, governed data solutions that drive customer value.

Build scalable, secure Snowflake data pipelines and ETL/ELT processes across projects. Responsibilities include data modeling, performance tuning, advanced SQL development, data quality, security, and work with structured and semi-structured data. The role also contributes to Center of Excellence initiatives, reference architectures, guidelines, proof-of-concepts, and collaboration with architects, engineers, and business teams. SnowPro certification and extensive Snowflake expertise are required.

Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support data platform architecture, governance, security, performance tuning, and deployment across AWS, Azure, or GCP. Collaborate with data scientists, analysts, architects, and engineers to ensure reliable, secure, and accessible data.

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, develop, and maintain data solutions and ETL pipelines using PySpark. Ensure data quality, integrate and migrate data across systems, collaborate with cross-functional teams, make technical decisions, mentor juniors, and act as an SME for multiple teams.

The role involves enhancing application systems, providing solutions, consulting with clients, and analyzing vulnerabilities in applications while advising lower-level analysts and ensuring compliance with regulations.

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.

Design, develop, deploy, and maintain scalable real-time data pipelines using Java, Apache Kafka, and Apache Flink. Build low-latency, high-throughput streaming solutions and event-driven applications for analytics and operational reporting. Collaborate with architects and stakeholders, monitor and troubleshoot production systems, optimize performance and fault tolerance, and implement data quality, security, governance, and operational best practices.

Designs and maintains SAP BW/4HANA data models and pipelines, develops end-to-end BI solutions, integrates SAP and non-SAP data, and delivers structured datasets for reporting and analytics. The role includes ETL, data transformation, performance optimization, query and load monitoring, and collaboration with business stakeholders. Candidates need strong SQL, data warehousing, SAP Datasphere, SAP BW on HANA, and SAP BDC experience. Power BI, Business Objects, and ABAP are beneficial.

Design, build, deploy and maintain backend data applications and services on cloud-based analytics platforms. Collaborate with product owners, troubleshoot and optimize environments, enable end-user productivity, support CI/CD and automation, and coach users on tools and data.

Design, develop, and maintain Hadoop-based data platforms and a TigerGraph/Neo4j GraphRAG solution. Monitor and optimize daily batch processes, ensure system stability and performance, support deployments/upgrades, perform query tuning, troubleshoot production issues, and maintain data governance and documentation. Collaborate with CIO leads, product teams, analysts, and data scientists.

Migrate legacy data warehouse implementations to Databricks by interpreting T-SQL logic, converting transformations into optimized PySpark/Databricks pipelines, designing ETL/ELT jobs, performing data validation and testing, optimizing performance, documenting migration logic, and collaborating with architects and business teams.

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.