ETL Testing with AWS Ecosystem

Job Title: ETL Tester AWS Ecosystem Experience: 3–4 Years Location: Pune Employment Type: Full-Time Job Summary: We are looking for a skilled ETL Tester with hands-on experience in validating data pipelines and data transformations in an AWS-based ecosystem . The ideal candidate should have a strong background in ETL testing, a solid understanding of data warehousing concepts, and proficiency with tools and services in AWS like S3, Redshift, Glue, Athena, and Lambda. Key Responsibilities: Design and execute ETL test cases for data ingestion, transformation, and loading processes. Perform data validation and reconciliation across source systems, staging, and target layers (e.g., S3, Redshift, RDS). Understand data mappings and business rules; write SQL queries to validate transformation logic. Conduct end-to-end testing including functional, regression, and performance testing of ETL jobs. Work closely with developers, data engineers, and business analysts to identify and troubleshoot defects . Validate data pipelines orchestrated through AWS Glue, Step Functions, and Lambda functions . Utilize Athena and Redshift Spectrum for testing data stored in S3. Collaborate using tools like JIRA, Confluence, Git, and CI/CD pipelines . Prepare detailed test documentation including test plans, test cases, and test summary reports. Required Skills: 3–4 years of experience in ETL/Data Warehouse testing . Strong SQL skills for data validation across large datasets. Working knowledge of AWS services such as S3, Redshift, Glue, Athena, Lambda, CloudWatch. Experience testing batch and streaming data pipelines . Familiarity with Python or PySpark is a plus for data transformation or test automation. Experience in using ETL tools (e.g., Informatica, Talend, or AWS Glue ETL scripts). Knowledge of Agile/Scrum methodology . Understanding of data quality frameworks and test automation practices . Good to Have: Exposure to BI tools like QuickSight, Tableau, or Power BI. Basic understanding of data lake and data lakehouse architectures . Experience in working with JSON, Parquet , and other semi-structured data formats.