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Tier-1 brand and Pune metro raise competition, but senior niche data QE specialization limits applicant pool.
Specialized data virtualization, BI and AI-testing expertise significantly reduces cross-industry transferability.
Explicit 10-12 years plus mandatory data QE, BI and platform expertise enforces strict filtering.
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Lead and own the end-to-end automated testing strategy for AI-powered data virtualization, federation, BI/reporting, and conversational AI query interfaces.
Manage and mentor a specialized team of data and report quality engineers, establishing quality governance, standards, and automated quality gates.
Integrate and maintain test automation suites within CI/CD pipelines and report quality metrics and test maturity to senior global leaders.
10-12 years of total relevant experience in software testing, quality or data engineering.
5-8 years hands-on experience in automated data testing, ETL testing, or data pipeline quality engineering.
5-8 years experience in BI/reporting testing including data reconciliation and source-to-target validation.
Bachelor’s degree in Computer Science, Information Systems, or equivalent engineering discipline.
Demonstrated expertise operating in complex enterprise data ecosystems involving virtualization platforms such as Starburst, Trino, Presto, or Denodo.
Strong leadership in Agile QE environments managing multi-layered test strategies for large-scale data platform migrations.
Proficient in both technical hands-on automation (Python/Java, SQL) and strategic quality governance within cross-functional technology teams.