QA Engineer- Data Engineering

About The Role We're looking for a QA Engineer to own quality assurance for our data engineering projects, while also providing QA support across other project teams as needed. You'll work closely with data engineers to validate pipelines and data products, and step in on functional testing for web and app projects when required. This role is ideal for someone who is detail-oriented, comfortable working with data at scale, and able to juggle priorities across multiple teams.

Responsibilities

• Validate data pipelines end-to-end: source-to-target checks, transformations, data completeness, accuracy, and reconciliation
• Write and execute SQL queries to test data quality across ETL/ELT workflows
• Design and maintain test plans, test cases, and traceability documentation for data projects
• Build automated data quality checks and integrate them into pipeline workflows
• Test reports and dashboards (e.g., Power BI/Tableau) against source data to verify metrics and business logic
• Perform smoke, regression, and integration testing after pipeline deployments and schema changes
• Support functional, regression, and UAT testing on other projects (web/app) when required
• Log, track, and verify defects; work closely with data engineers and developers through resolution
• Participate in requirement reviews to identify testability gaps and edge cases early
• Contribute to improving QA processes, standards, and documentation across teams

Requirements

• Experience testing data pipelines, ETL, or data warehouse projects
• Strong SQL skills — able to write complex queries for data validation independently
• Understanding of data warehousing concepts (schemas, transformations, incremental loads, slowly changing dimensions)
• Experience with test management and defect tracking tools (e.g., Jira)
• Ability to read and understand pipeline logic and data models to design meaningful tests
• Strong attention to detail and ability to switch context across multiple projects
• Good communication skills for working with cross-functional teams
• Exposure to Agile/Scrum ways of working

Nice to Have

• Experience with Azure data services (Azure Data Factory, Synapse, ADLS)
• Hands-on experience with Databricks (notebooks, Delta Lake, job runs)
• Test automation skills — Python, pytest, or data quality frameworks like Great Expectations or dbt tests
• Experience testing APIs (e.g., Postman) or basic web automation (e.g., Selenium/Playwright)
• Familiarity with CI/CD pipelines (e.g., Azure DevOps, GitHub Actions)