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Tier-1 brand, metro location, broad skills and senior-level requirements increase competition.
Specialized data virtualization, BI and AI testing increases domain specificity, reducing cross-industry transferability.
Explicit 10–12 year minimum, specialized data-QE skills, and leadership make filters very strict.
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Lead strategy and execution of automated testing for data virtualization, federated queries, BI/reporting, and AI-driven analytical interfaces.
Manage and mentor a specialized team of quality engineers ensuring testing standards, governance, and quality metrics reporting to senior leadership.
Design and implement test automation frameworks covering complex data architectures including data contract validation, security testing, conversational AI query interfaces, and large-scale data/report platform migrations.
10-12 years of total experience in software testing, quality engineering, or data engineering.
5-8 years of hands-on experience in automated data testing, ETL testing, or data pipeline quality engineering.
5-8 years of experience in BI/reporting testing, data reconciliation, and source-to-target validation.
Bachelor’s degree in Computer Science, Information Systems, or equivalent engineering field.
Proven leader with experience managing QE teams for data-heavy, AI-powered analytics or reporting platforms.
Hands-on expertise with data virtualization/federation platforms (e.g., Starburst, Trino, Denodo), advanced SQL, and integrating automated tests into CI/CD pipelines.
Strong background in designing multi-layered test strategies across complex, large-scale data ecosystems including conversational AI and data security testing.