Quality Engineering Lead – Data & Reporting Platforms

Role Overview We are seeking a highly skilled and experienced VP, Quality Engineering Lead to define, build, and drive our automated data and report testing strategy. In this role, you will lead the Quality Engineering (QE) initiatives for our next-generation, AI-powered data and reporting ecosystem. As a hands-on leader, you will design robust automated test suites to validate complex data architectures—specifically focusing on data virtualization, massive data federation, data contract testing, and the verification of emerging natural language/conversational AI query interfaces. You will manage a talented team of quality engineers, establish testing standards, and collaborate closely with engineering, and product teams to ensure high-quality, secure, and performant data and report delivery. Key Responsibilities 1. Test Strategy & Quality Leadership Data & Reporting Test Strategy: Architect and execute a comprehensive, end-to-end automated testing strategy covering data virtualization, federated queries, BI/reporting, and AI-enabled analytical interfaces. Team Leadership: Lead, mentor, and functionally manage a specialized team of Data & Report Quality Engineers, fostering a culture of modern Quality Engineering (QE) and continuous improvement. Governance & Compliance: Define operating standards, automated quality gates, and data verification protocols across the analytics and reporting delivery lifecycle. Stakeholder Management: Own the reporting of quality metrics, pipeline coverage, and test automation maturity to senior global technology and engineering leaders. 2. Data Virtualization & Federation Testing Federated Query & Virtualization Validation: Develop automated testing frameworks to validate query execution, latency, and data integrity across massive federated query engines and data virtualization platforms (e.g., Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill) connecting dozens of heterogeneous catalogs without physical data movement. Data Contract & Schema Validation: Implement automated schema validation and data contract testing to ensure curated, virtualized data products strictly adhere to published business definitions and system requirements. Access Control & Security Testing: Design data-driven security tests to verify that centralized data access governance (e.g., Apache Ranger, role/attribute-based access controls) and data masking are flawlessly applied. 3. AI & Conversational Intelligence Testing Natural Language Query Testing: Establish frameworks to test conversational AI interfaces that allow users to query data using natural language. Validate natural language processing (NLP) models, intent recognition, NLP-to-SQL translation logic, and the accuracy of the underlying datasets returned.