Job Location : Chennai, Pune, Noida, Kochi, Bangalore, Trivandrum

Experience : 6 Yr

CTC Budget : 2500000 to 2500000

Posted At : 17-Dec-2025

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We are looking for an experienced Tech Lead – Data Engineering to design, lead, and deliver scalable data solutions in a modern cloud environment. The ideal candidate will have deep hands-on expertise in ETL/ELT development, data lake architecture, and data warehousing, along with strong command over AWS data services, Python, and Spark/Databricks.

The candidate will act as a technical lead and mentor, guiding a team of 3–7 engineers, ensuring delivery excellence, and aligning technical execution with architectural best practices and organizational data strategy.

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Key Responsibilities

• Lead the end-to-end design and delivery of modern data engineering solutions, ensuring performance, scalability, and reliability.

• Architect and develop ETL/ELT pipelines using tools such as AWS Glue, DBT, and Airflow, integrating multiple structured and semi-structured data sources.

• Design and maintain data lakes and data warehouse environments on AWS (S3, Redshift, Athena, Glue).

• Build and optimize Spark / Databricks jobs for large-scale data transformation and processing.

• Define and enforce best practices in coding, version control, testing, CI/CD, and data quality management.

• Oversee infrastructure setup and automation using Terraform, Kubernetes, and Docker for data environments.

•• Manage and mentor a team of 3–7 engineers, conducting technical reviews, workload planning, and skill development.

• Monitor, troubleshoot, and optimize data pipelines in production to ensure reliability and SLAs.

• Drive continuous improvement initiatives for pipeline automation, observability, and cost optimization.

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Technical Skills and Tools

Core Technical Expertise:

• Programming: Python (preferred), SQL, and scripting for data transformation and automation.

• ETL/ELT & Orchestration: AWS Glue, DBT, Airflow, Step Functions.

• Cloud Platforms: AWS (S3, Glue, Lambda, Redshift, Athena, EMR), exposure to Azure Data Services a plus.

• Data Processing: Apache Spark, Databricks.

• Databases: PostgreSQL, Snowflake, MongoDB.

• CI/CD & DevOps: GitHub Actions, CircleCI, Jenkins, with automation via Terraform and Docker.

• Infrastructure Management: Kubernetes, Terraform, CloudFormation.

• Data Modeling & Warehousing: Dimensional modeling, partitioning, and schema design.

Candidate should have 7+ years in Data 

5+ years in AWS Data Services

5+ years in Spark 

3+ years in Team Handling