QA Automation - ETL Testing

Purpose The Quality Engineer (QE) will play a key role in establishing a scalable, automation-driven QA framework across Data Intelligence initiatives. This role focuses on improving data quality, automation test efficiency, and release confidence for Azure-based platforms, including Databricks, Power BI, and Azure Functions.

The QE will enable a shift-left testing approach, integrating QA early into the development lifecycle to ensure data integrity, system reliability, and continuous delivery alignment within a cloud-first environment. This is a hands-on engineering role for an experienced QE with deep data testing expertise, strong automation skills, and proven experience integrating QA into CI/CD pipelines.

The resource will partner with Data Engineering, Analytics, and Development teams to deliver measurable improvements in quality, automation coverage, and defect prevention. Key Deliverables Automated Test Framework Development: Design and implement reusable, scalable automation frameworks covering ETL pipelines, APIs, UI, and back-end data systems using Selenium and related tools.

• End-to-End Test Coverage: Provide comprehensive manual and automated test coverage across Dev, QA, and Production environments, ensuring validation of all data processing and reporting layers.
• Data Validation & Quality Assurance: Conduct deep data validation across Databricks, Power BI, and Azure data flows to ensure accuracy, completeness, and consistency. Implement automated ETL testing to detect data issues early.
• CI/CD Integration: Integrate automated testing into CI/CD pipelines using GitHub Actions to support continuous testing, deployment validation, and faster release cycles.
• QA Process Standardization: Apply standardized QA processes, templates, and governance models aligned with enterprise QA best practices.
• Jira-Based QA Management: Leverage Jira for complete QA lifecycle management, including test planning, case management, defect tracking, and AI-assisted test creation. Use Confluence for documentation and traceability.
• AI-Augmented Testing: Utilize AI tools within Jira to automate test design, optimize test case selection, and improve overall test efficiency and coverage.
• Reporting & Visibility: Provide regular QA status updates, risk assessments, and mitigation plans. Maintain dashboards for test coverage, automation ROI, and defect trends
• Performance & Resilience Testing: Conduct performance and load testing where applicable to ensure reliability of data services. Support DR testing and validation of environment stability.

Required Skills

& Experience Strong knowledge of test automation and data validation.

• Expertise in Selenium, Postman, GitHub, and GitHub Actions for CI/CD testing automation.
• Hands-on experience testing in Azure environments, including Databricks, Azure Functions, and Power BI.
• Strong SQL proficiency for data analysis, test data management, and validation.
• Experience executing tests across Agile/Scrum delivery models.
• Familiarity with AI tools for QA, Jira, Confluence, SharePoint, and Git-based repositories.

Preferred Qualifications

Proven experience building or optimizing CI/CD pipelines for automated testing.

• Background in data governance, masking, and compliance (PHI, PII).
• Experience with performance/load testing tools such as JMeter or Blaze. Meter.
• Exposure to AI/ML or data science model validation. Soft Skills & Attributes
• Strong analytical, documentation, and communication skills.
• Detail-oriented and highly organized with an automation-first mindset.
• Proactive, self-driven, and capable of operating independently in a fast-paced environment.
• Committed to continuous improvement and measurable quality outcomes.