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Tier-1 employer and Pune metro increase competition, but seniority and niche data/BI QA reduce applicant pool.
Specialized data-virtualization, federation, NLP, and BI testing demands domain-specific experience, limiting cross-industry transferability.
Explicit seniority, mandatory domain experience, and specific tool/technology requirements create highly stringent shortlisting filters.
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Lead design and execution of automated testing strategy for AI-powered data and reporting platforms, focusing on data virtualization, federated queries, and AI query interfaces.
Manage and mentor a specialized quality engineering team, establishing testing standards and collaborating with engineering and product teams to ensure data/report quality, security, and performance.
Integrate automated testing suites into CI/CD pipelines and oversee quality metrics reporting and defect triage across data/reporting systems.
10-12 years total software testing, quality engineering, or data engineering experience.
5-8 years hands-on automated data testing, ETL testing, or data pipeline quality engineering.
5-8 years experience in BI/report testing, data reconciliation, and source-to-target data validation.
Bachelor’s degree in Computer Science, Information Systems, or equivalent engineering field.
Proven leadership in managing QE teams and driving multi-layered testing strategies across large-scale data/reporting migrations.
Deep technical expertise in data virtualization platforms (e.g., Starburst, Trino), BI/reporting tools (e.g., Tableau), and advanced SQL/database testing.
Experience integrating automated testing within Agile frameworks and enterprise CI/CD environments.