Position Title: Lead Quality Engineer – Data, BI & AI Automation
Location: Manhattan Beach, CA
Purpose of the Position:
Purpose of the Position:
The purpose of this role is to establish an embedded, AINative, automationfirst Quality Engineering capability within the BI development lifecycle. The AINative QE Lead will drive endtoend quality assurance across dashboards, reports, KPIs, and underlying data pipelines, eliminating traditional QA handoffs and accelerating release cycles through agentic automation. This role will enable automated functional, nonfunctional, and data validation, regression engineering, and autonomous defect management, ensuring reliable, businessready BI releases.
By enabling consistent quality scorecards, governance rhythms, and measurable KPIs such as automation coverage, cycle time, and defect leakage, the role establishes objective quality visibility. Working in close partnership with BI development, data engineering, and business stakeholders, the QE Lead transforms QA from a manual, reactive function into a proactive, automated, AIdriven Quality Engineering POD and ultimately reducing release effort, increasing reliability, and strengthening BI ecosystem.
Key Result Areas and Activities:
Automation-First Test Planning and Execution:
- Design and execute automationfirst test strategies covering BI dashboards, KPIs, reports, and endtoend data pipelines
- Implement AIdriven test case generation, edgecase identification, and autonomous defect triage.
- Build, maintain, and optimize regression packs for continuous, accelerated BI releases.
- Ensure automated coverage of functional and nonfunctional requirements—including performance, data integrity, and usability.
Process Transformation and Continuous Improvement:
- Lead the shift from QA → QE by defining automation baselines, maturity goals, and improvement roadmap.
- Define and track KPIs such as Agentic automation coverage, Cycle time reduction, Defect leakage, Regression pack efficiency.
- Establish reusable frameworks, templates, accelerators, and QE governance processes.
- Identify bottlenecks and enable continuous improvement through automation and process redesign.
AI-Native QE Leadership and POD Enablement:
- Lead the QE POD endtoend: planning, task allocation, oversight, coaching.
- Champion the adoption of agentic testing and autonomous validation workflows.
- Maintain strong alignment with BI developers, data engineering, and business teams.
- Ensure skill elasticity by planning monthly capability needs (as defined in governance forum).
- Provide technical oversight for Python, SQL, and AIdriven automation tooling.
Quality Governance, Visibility, and Stakeholder Engagement:
- Maintain and publish quality scorecards tracking for Coverage, Cycle time, Defect trends, Automation footprint.
- Conduct regular governance meetings (weekly/biweekly/monthly).
- Serve as the point of contact for all BI quality assurance and QE metrics.
- Ensure alignment to contractual KPIs tied to performancelinked incentives.
Stakeholder Collaboration and Business Alignment:
- Collaborate with business SMEs for requirement elaboration and testcase definition.
- Partner closely with Data Engineering to understand upstream impacts.
- Participate in sprint planning, grooming, and UAT cycles.
- Ensure testing readiness by validating documentation, functional specs, and design changes.
- Facilitate smooth transitions and handoffs across BI development lifecycle.
Essential Skills:
- Strong experience designing and implementing automationfirst test strategies across functional, nonfunctional, data, and regression testing scope.
- Proven experience to leverage AI agents for test case generation, edge case discovery, autonomous defect validation, and regression acceleration.
- Deep QE expertise for BI dashboards, KPIs, reports, and underlying semantic data models.
- Strong handson experience with ETL/ELT validation, endtoend data quality checks, and pipelinelevel verification.
- Proficiency in SQL and Python for test automation, data sampling, and validation logic scripting.
- Experience defining and building profiling rules, reconciliation logic, and dataintegrity validations across multilayer pipelines
- Handson expertise creating and maintaining regression packs and reusable automation assets.
- Strong collaboration and communication skills for working with BI developers, data engineering teams, and business SMEs.
Qualifications:
- Bachelor s degree in computer science, Information Technology or related field.
- Minimum of 8+ years of experience in a Testing/QA role, with a focus on BI and ETL testing.
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