Job Description
Key Responsibilities: •
Perform data validation and reconciliation across source-to-target pipelines using SQL and automated checks •
Design and execute test strategies for data quality pipelines, including test data preparation, labeling verification, and schema validation
Validate predictive quality metrics (trend forecasts, risk scoring, defect leakage signals) for correctness and stability •
Execute defect trend analysis and risk-based testing using telemetry and historical patterns • Implement automated data validation in CI/CD pipelines and ensure continuous quality gates
- Conduct prompt testing and A/B comparisons for AI-assisted analytics interactions (where applicable)
- Leverage LLMs for: • Intelligent test data generation •
- Automated data labeling validation
- • AI-assisted anomaly detection in data quality pipelines
- • Produce actionable insights via statistical thinking and analytical reasoning; communicate quality risks clearly to stakeholders
Must Have Tools •
SQL (Postgres / Snowflake) • Pandas / NumPy • Python (data validation) • Promptfoo (A/B prompt tests) • Test data profiling tools • OpenAI / Azure OpenAI APIs • Cursor AI / GitHub Copilot / Claude CLI
No Referrers Available
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