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Design, develop, and optimize large-scale data pipelines and ETL workflows on AWS.

Requirements

• 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.
• Familiarity with CI/CD pipelines for data engineering (CodePipeline, Jenkins, GitHub Actions).
• Knowledge of BI/Visualization tools like Tableau, Power BI, QuickSight.

Responsibilities

• 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.
• Ensure compliance with data security, governance, and regulatory requirements (BFSI preferred).
• Troubleshoot production issues and optimize pipeline performance.

Other

• 12+ years of experience in Data Engineering, with at least 5+ years on AWS cloud data services.
• Strong problem-solving skills and ability to work in Agile/Scrum environments.
• Experience in BFSI / Wealth Management domain.
• AWS Certified Data Analytics – Specialty or AWS Solutions Architect certification.
• Bachelor’s degree in Computer Science, Information Technology, Engineering, or related field.