Key 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.
· 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.
Required Qualifications
· 9+ 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.
Preferred Qualifications
· AWS Certified Data Analytics – Specialty or AWS Solutions Architect certification.
· Familiarity with CI/CD pipelines for data engineering (CodePipeline, Jenkins, GitHub Actions).
· Knowledge of BI/Visualization tools like Tableau, Power BI, QuickSight.
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