QA Manager, Enterprise Technology and AI

An overview of this role

The Enterprise Technology QA Manager is responsible for defining and leading the quality engineering strategy for GitLab’s business applications ecosystem, with strong emphasis on functional testing, automation testing, and AI-driven QA practices. This role partners across Enterprise Applications, PMO, AI & Automation team, and business stakeholders to improve release quality, reduce change risk, and increase confidence across systems that run critical business processes.

What you'll do

• Define and lead the QA strategy for Business Applications, with accountability for functional quality, automation quality, release confidence, and defect prevention across the application portfolio.
• Build and scale a quality engineering practice that supports enterprise platforms such as Salesforce, Zuora and related systems and integrations that power core business operations.
• Establish strong functional testing practices for end-to-end business processes, including configuration validation, workflow testing, role-based access validation, integration testing, and data reconciliation across systems.
• Lead the design and expansion of automation testing frameworks and reusable automated test assets for UI, API, integration, and regression test coverage.
• Partner with Business Applications, PMO, AI & Automation and business stakeholders to embed quality earlier in the delivery lifecycle and ensure clear release-readiness criteria for projects, enhancements, and production changes.
• Own QA planning and execution for complex cross-functional processes spanning CRM, Billing and Support systems, and supporting data flows that impact business continuity and customer outcomes.
• Track and communicate quality metrics including test coverage, test gaps, defect leakage, automation effectiveness, release quality, and team productivity, and use those insights to drive continuous improvement.
• Define and implement an AI-driven QA approach, including opportunities for AI-assisted test design, regression impact analysis, defect prediction, and intelligent test execution optimization.