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Mid-level generalist QA role, metro location, and common automation skill requirements increase candidate competition.
Skills are transferable across industries but require domain-specific ETL and data validation knowledge.
Requires specific data QA automation, CI/CD and tooling skills but lists no explicit years requirement.
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Own design and maintenance of automated validation frameworks for ETL pipelines and large-scale data systems ensuring data accuracy and reliability.
Develop and execute data quality automation checks integrated into CI/CD pipelines, covering regression, schema, and contract validations.
Leverage Generative AI to enhance test case generation, anomaly detection, root cause analysis, and to track data quality KPIs and automation effectiveness.
Experience with automated data quality validation in ETL pipelines and big data environments.
Strong hands-on coding skills in JavaScript.
Proficiency in SQL and database validation including data reconciliation and integrity checks.
Work Experience Required: Not explicitly mentioned in the JD.
Operationally focused on building scalable automation solutions integrated with CI/CD for data quality assurance.
Experienced in handling complex data transformations on high-volume datasets in an engineering team environment.
Comfortable working with Generative AI tools to improve automation coverage, detecting edge cases and anomalies.