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Tier-1 brand and metro hiring increase interest, but senior niche data-QE skills limit applicant density.
Deep data virtualization, BI testing, and QE leadership requirements make cross-industry transfers difficult.
Multiple explicit years minima and mandatory specialized data-testing platforms create stringent shortlisting filters.
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Define and execute automated testing strategies for data virtualization, federation, BI/reporting, and AI-enabled analytical interfaces.
Lead and manage a team of quality engineers focused on data and report quality assurance across complex data architectures.
Integrate automated test suites into CI/CD pipelines and report quality metrics to senior technology and engineering leaders.
10-12 years in software testing, quality engineering, or data engineering with 5-8 years in automated data testing and BI/reporting validation.
Bachelor’s degree in Computer Science, Information Systems, or equivalent engineering field.
Hands-on experience with data virtualization/federation platforms (Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill).
Proficiency in SQL, Python or Java, API testing (REST/SOAP), and CI/CD integration for test automation.
Experienced leader capable of managing specialized teams and driving quality engineering in advanced data and AI-powered reporting environments.
Demonstrated ability to architect comprehensive multi-layered test strategies including automation for complex, federated data platforms and AI query interfaces.
Strong track record integrating testing frameworks into enterprise CI/CD pipelines and collaborating across engineering and product teams.