About the role
The Data QA Engineer will drive adoption of AI-powered tools and intelligent automation to enhance testing efficiency, coverage, and defect detection throughout the software development life cycle. Provide data governance, quality leadership, and guidance to the Data team, while advocating for measurable improvements in product quality through modern QA practices.
What you'll do
- Leverage AI-powered tools and generative AI to automate test case generation, prioritize high-risk scenarios, and minimize manual effort.
- Implement intelligent automation (e.g., self-healing tests, AI-driven anomaly detection) to enhance test reliability and speed.
- Research and integrate emerging AI tools to streamline data validation, regression testing, and quality reporting.
- Collaborate with the team to evolve testing toward highly automated, AI-augmented processes.
- Ensure end-to-end data integrity and completeness validation across pipelines.
- Conduct gap analysis, data mapping, process documentation, and flow charting.
- Enforce quality control for all software deliverables and elevate QA standards/best practices.
- Deliver actionable insights and sprint quality reports; drive retrospective discussions for continuous improvement.
Tasks
- Use AI tools (generative AI, intelligent platforms) to auto-generate/maintain SQL queries, test scripts, and validation scenarios.
- Build and maintain advanced Python-based automated test suites with AI for smarter selection, flaky test detection, and coverage optimization.
- Experiment with AI-assisted root cause analysis for data issues and incidents.
- Track and report automation coverage, AI-driven efficiency gains, defect trends, and sprint quality metrics.
- Create, execute, and maintain tests targeting data completeness, integrity, and pipeline health.
- Act as primary QA contact at squad level; participate in all sprint rituals and facilitate retro discussions.
- Conduct regular syncs with interns, Development Lead, and Product Owners.
- Maintain comprehensive test suites, documentation, and quality artifacts.
- Drive resolution of production incidents (SUPIN/RCA exercises)
Qualifications
- 3+ years in a data quality or QA engineering role in software development.
- Proven experience with AI-powered testing tools (e.g., Testim, Mabl, Applitools, Functionize, CodiumAI etc) to boost automation and efficiency.