about 1 month ago
Responsibilities
- Develop expertise in the enterprise data model, business domains, and data flows.
- Own end-to-end quality for data products published through the Enterprise Data Platform.
- Design and implement automated testing frameworks for data pipelines, transformations, APIs, and platform capabilities.
- Validate data correctness, completeness, consistency, freshness, and lineage across the data platform.
- Build scalable reconciliation frameworks comparing data lake outputs with operational databases, product UIs, upstream services, business reports, and dashboards.
- Define data quality metrics, validation rules, and automated monitoring for anomaly detection.
- Partner with engineering teams to validate ingestion, transformations, data sharing, governance, and performance capabilities.
- Design regression suites for large-scale data platform changes and migrations.
- Lead root cause analysis for production data quality issues and collaborate on preventive improvements.
- Define and execute test strategies for AI-powered Analytics Agent capabilities, including user-intent validation, evaluation datasets, response accuracy and consistency measurement, hallucination detection, edge-case identification, and evaluation-coverage improvement.
- Champion quality engineering best practices across the Data Platform organization.
Requirements
- 5+ years of experience in SDET for data engineering and data platforms.
Categories
Data EngineeringTesting