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Metro location and mid-level SDET role increase competition, but data/ML specialization narrows candidate pool.
Testing and automation skills transfer across industries, but data-platform and AI evaluation needs domain knowledge.
Role requires specialized data-platform and automated testing skills but lists no strict years requirement.
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Own end-to-end data quality and reliability for enterprise data products within the data platform.
Design and implement automated testing and validation frameworks for data pipelines, transformations, APIs, and AI capabilities.
Drive root cause analysis and preventive improvements for data quality issues, collaborating closely with Data Engineering and AI teams.
Work Experience Required: Not explicitly mentioned in the JD.
Experience with automated testing frameworks for data pipelines and data quality validation.
Knowledge of data quality metrics, validation rules, and anomaly detection.
Familiarity with AI/ML evaluation strategies including user intent validation and accuracy measurement.
Strong understanding of enterprise data models and business domain data flows relevant to automotive or large-scale data platforms.
Experience working closely with cross-functional teams including Product Management, Analytics, and AI.
Ability to lead quality engineering initiatives in complex data environments with AI-powered product components.