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Mid-level Bangalore role but niche data/AI QA specialization limits broad applicant pool.
Domain-specific data and AI QA skills transferable across data-heavy companies but require relevant platform experience.
Requires domain-specific data platform and AI evaluation skills but no explicit years, so filters are moderately strict.
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Own quality assurance for enterprise data products, ensuring correctness, completeness, and consistency of data across the platform.
Design and implement automated testing and validation frameworks for data pipelines, transformations, APIs, and AI/ML-powered analytics features.
Lead root cause analysis and preventive improvements for production data quality issues, working closely with engineering and product teams.
Experience Requirement: Not explicitly mentioned in the JD
Strong knowledge of data quality testing, data validation, and automated testing frameworks for data platforms.
Familiarity with data reconciliation techniques and monitoring for data anomaly detection.
Experience or willingness to test AI-powered analytics components, including user intent validation and evaluation dataset creation.
Deep understanding of enterprise data models, business domains, and data flow architectures in large-scale data lakes.
Experienced in designing scalable, automated validation frameworks for complex, distributed data platforms involving multiple data sources.
Comfortable working cross-functionally with Data Engineering, Product, Analytics, and AI teams to ensure high data quality and platform reliability.