Skills and Competencies :

- 6+ years of experience in Software Quality Assurance or Data Quality Engineering, including automation - first testing for complex enterprise data platforms

- Strong hands - on experience with Databricks SQL and Python, including testing of notebooks, workflows, pipelines, and scheduled jobs

- Solid understanding of Medallion architecture (Bronze, Silver, Gold) and data lifecycle validation practices across enterprise data ecosystems

- Expertise in PySpark for data validation, test automation, and building scalable, reusable testing frameworks

- Strong proficiency in SQL and data validation techniques across dataset, record, schema, transformation, and source - to - target mapping levels

- Experience working in Agile or Scrum environments with tools such as Xray for test management and Jira for defect tracking

- Proven experience leading quality assurance initiatives, mentoring team members, driving quality governance, managing stakeholder expectations, and influencing delivery decisions

- Demonstrated proficiency in artificial intelligence concepts, with hands - on experience using AI tools to streamline workflows and enhance operational efficiency.

- Proven ability to implement AI - powered solutions to solve business challenges.

- Demonstrates a growing awareness of AI risk management and a commitment to responsible and ethical AI use.

Education :

- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field

Responsibilities :

- Ensure data quality, reliability, and correctness across large - scale data platform pipelines through automated validation and testing practices

- Lead end - to - end data quality validation across Bronze, Silver, and Gold data layers, establishing testing standards and quality gates across projects

- Define and drive the overall quality assurance strategy, test approach, automation roadmap, and release readiness criteria for enterprise data platform initiatives

- Mentor and provide technical leadership to quality assurance engineers by reviewing test plans, automation frameworks, validation logic, defect analysis, and quality metrics

- Collaborate with engineering, platform, product, and architecture teams to influence solution design, identify quality risks early, and ensure testability of data solutions

- Own quality governance activities including test execution reviews, defect triage, risk assessments, root cause analysis, and executive - level status reporting

- Drive continuous improvement initiatives by identifying opportunities to enhance automation coverage, testing efficiency, data quality controls, and operational excellence

- Act as the primary quality assurance point of contact for stakeholders, providing leadership during releases, production validation, issue resolution, and audit or compliance reviews