About this role

Design, develop, and maintain ETL pipelines using AWS Glue, Glue Studio, and Glue Catalog.
Ingest, transform, and load large datasets from structured and unstructured sources into AWS data lakes/warehouses.
Work with S3, Redshift, Athena, Lambda, and Step Functions for data storage, query, and orchestration.
Build and optimize PySpark/Scala scripts within AWS Glue for complex transformations.
Implement data quality checks, lineage, and monitoring across pipelines.
Collaborate with business analysts, data scientists, and product teams to deliver reliable data solutions.
Ensure compliance with data security, governance, and regulatory requirements (BFSI preferred).
Troubleshoot production issues and optimize pipeline performance.

12+ years of experience in Data Engineering, with at least 5+ years on AWS cloud data services.
Strong expertise in AWS Glue, S3, Redshift, Athena, Lambda, Step Functions, CloudWatch.
Proficiency in PySpark, Python, SQL for ETL and data transformations.
Experience in data modeling (star, snowflake, dimensional models) and performance tuning.
Hands-on experience with data lake/data warehouse architecture and implementation.
Strong problem-solving skills and ability to work in Agile/Scrum environments.

AWS Data Engineer

Key skills