Data Warehouse QA Lead
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About the role
Lead the Enterprise Data Warehouse (EDW) QA practice end to end, including the QA Leads embedded in each delivery team, the automation engineers, and the shared engineer pool. Own and continuously improve the shift-left, test-driven operating model: QA engagement from backlog refinement, acceptance criteria and test design agreed before code is written, and stage-by-stage QA responsibilities integrated with the Enhancement Team delivery process.
Key responsibilities
- Define, baseline and report the practice service level objectives, including first-pass quality rate, defect escape rate and automated regression coverage, and drive the improvement plan against them
- Own the data quality CI/CD gate standards (accuracy, latency, performance, security) applied on every environment promotion, and the framework used to enforce them
- Represent QA in the service governance cadence with US Enterprise Data stakeholders: steering committee, delivery reviews, escalations and maturity assessments
- Plan capacity across the four delivery teams, balance the shared engineer pool against demand, and manage sprint level QA commitments
- Set the test strategy, standards, templates and tooling for data validation, regression, source to target, performance and security testing across SQL Server, SSIS, dbt, Fivetran, Python ingestion and the Databricks Lakehouse
- Own the QA knowledge base, onboarding programme and skills matrix; run the certification and upskilling track including Databricks readiness
- Recruit, coach, appraise and develop QA Leads and engineers; run performance and career conversations
- Champion responsible use of AI/LLM-powered tools (e.g., Claude, Copilot) for test design, synthetic data and query review, with validation standards for AI-assisted output before use in production test cycles
- Act as the escalation point for quality risks, release readiness disputes and post-production defects; lead root cause analysis and corrective action
- Communicate practice status, metrics and risks to ABC leadership and US stakeholders; present demos, timelines and insights
Requirements:
- At least 10+ years in software or data quality assurance, with 4+ years leading QA teams in a data engineering or data warehousing environment
- Proven track record establishing or transforming a QA practice, including shift-left or test-driven approaches and QA embedded in agile delivery teams
- Hands-on background in data testing with SQL and Python; able to review test designs and queries and challenge test coverage credibly
- Experience defining and reporting quality metrics and service level objectives to senior stakeholders
- Experience running QA in a managed service or shared service model with cross-border stakeholders
- Strong people leadership: hiring, coaching, performance management and succession planning
- Excellent verbal and written communication, including the ability to present to executive and non-technical audiences
- Familiarity with Agile/Scrum delivery and with Azure DevOps or equivalent work management tooling
- Bachelor's Degree in Computer Science, Information Systems, Computer Engineering or related field
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