Sr Quality Analyst - ETL Tester

Purpose of Job:

As an ETL Tester, you will play a critical role in ensuring the accuracy, consistency, and reliability of data as it moves through our ETL pipelines — from extraction and transformation through to loading into the data warehouse. You'll be responsible for designing test scenarios, executing source-to-target validations, and writing advanced SQL queries (joins, subqueries, window functions, aggregations) to catch data quality issues such as duplicates, nulls, and referential integrity gaps before they reach production. Working within an Agile team, you'll partner with data engineers, and business analysts to validate transformation logic and business rules, support UAT and production validation, and track defects through tools like Jira or Azure DevOps or GitHub. This role is ideal for someone with 3–5 years of ETL/DW testing experience who thrives on precision, enjoys solving data puzzles, and wants to grow into cloud, Big Data, or automation-driven testing.

We'll trust you to:

• Validate ETL workflows (Extract, Transform, Load).
• Create and execute test cases and test scenarios.
• Perform source-to-target data validation.
• Write complex SQL queries to verify data accuracy.
• Test data transformation rules and business logic.
• Perform functional, regression, integration, and data quality testing.
• Log and track defects using tools like Jira or Azure DevOps or GitHub.
• Work closely with ETL developers, data engineers, and business analysts.
• Support UAT and production validation.

You'll need to have:

• Strong SQL (joins, subqueries, window functions, aggregations)
• ETL testing concepts
• Data Warehouse concepts (Fact & Dimension tables, Star/Snowflake schema)
• Working experience with Databricks, or Snowflake
• Unix/Linux commands
• Defect tracking tools (Jira, Azure DevOps, GitHub)
• Knowledge of Agile/Scrum methodology
• Basic understanding of APIs and automation is an added advantage.
• 3–5 years in ETL/Data Warehouse testing

Nice-to-Have Skills

• Python or PySpark
• Cloud platforms (Azure, AWS, GCP)
• CI/CD tools (Jenkins, Git)
• Big Data testing (Hive, Hadoop, Spark)