Lead QA SDET Data Engineering

Key Responsibilities Automation Technical Execution

• Design, build, and maintain Python-based automation frameworks for data and ETL validation.
• Automate source-to-target validation, reconciliation, and transformation checks across Snowflake pipelines.
• Build and maintain automated test coverage for Informatica ETL/ELT workflows.
• Integrate automated test suites into CI/CD pipelines for continuous execution.
• Review team members automation code for quality, reusability, and maintainability.

Quality Assurance

• Own test planning and execution strategy for the program: functional, regression, integration, and data-quality testing.
• Validate data completeness, accuracy, consistency, uniqueness, integrity, and timeliness.
• Manage defect lifecycle for the program: triage, prioritization, RCA on recurring/critical defects, tracking to closure.
• Assess release readiness against defined quality gates; provide go/no-go input to leadership.
• Maintain program-level QA dashboards (execution status, pass rate, coverage, defect aging, automation stability).
• Identify test coverage gaps and risk areas across data flows.

Data Domain

• Build strong working knowledge of the programs business-critical data flows.
• Translate business/data rules into concrete automated validation checks.
• Partner with Data Engineers, Developers, and Business Analysts to resolve data quality issues.

Leadership

• Lead and mentor a team of Senior QA SDETs.
• Facilitate program-level test status and quality reviews.
• Apply enterprise/organizational QA standards consistently within the team.
• Use AI-assisted testing tools where appropriate, with review of AI-generated outputs.

Required Skills

• Python - strong automation and framework development experience.
• Snowflake - strong hands-on experience in data validation/warehouse testing.
• ETL/ELT - strong understanding of pipelines and validation.
• Informatica - solid hands-on experience with ETL workflows.
• Strong SQL and data warehouse fundamentals.
• Data reconciliation, transformation, and profiling experience.

• Test planning and execution across functional / regression / integration testing.
• Defect management and RCA (program level).
• Quality gates and release-readiness assessment.
• QA metrics/dashboard reporting.

• CI/CD and DevOps integration.
• Cloud data platforms / data lakes.
• API testing.
• Exposure to AI-assisted testing tools.

Experience Key Competencies Competency Expected Level Python (Automation) Advanced/Expert SQL Expert Snowflake Advanced/Expert ETL/ELT Testing Advanced/Expert Informatica Advanced Data Quality/Validation Advanced/Expert QA Test Planning Execution Advanced Defect Management Advanced Quality Gates Release Readiness Advanced QA Metrics Dashboards Advanced Team Leadership Advanced