Lead Quality Engineer (Test Automation & Transformation)

Role overview Lead practical improvements to software quality across multiple enterprise teams, moving projects from manual-heavy testing toward dependable automation and responsible AI-assisted testing. This is a hands-on technical leadership role: you will set direction, build and debug tests yourself, and help teams adopt approaches that work within their systems and delivery constraints.

Responsibilities - Assess testing practices across teams, identify shared opportunities, and guide phased adoption of automation, AI-assisted testing, and improved test data practices. - Write and troubleshoot UI, API, integration, and data tests; investigate defects and support releases on active projects. - Set pragmatic engineering principles, review technical solutions, and advise on tools across varied architectures. - Build reusable test data tools and patterns, including builders, fixtures, seeding, and reset workflows for complex systems. - Apply AI to test design, case and script generation, synthetic data, and failure analysis, with validation and safe-use practices. - Coach engineers through pairing and reviews, and establish reusable approaches for frameworks, CI/CD, reporting, and governance.

Requirements - Strong hands-on experience in quality engineering and test automation across UI, API, integration, or data testing. - Ability to solve challenging automation and test data problems in established systems and live delivery environments. - Experience setting technical direction across teams while adapting to differing project constraints. - Clear communication, sound judgment about AI and security, and the ability to mentor engineers and engage senior stakeholders. - Interest in remaining an individual technical contributor who builds and tests software, rather than focusing only on strategy or people management.

Nice to have - Experience improving quality practices across multiple teams or client environments. - Experience applying AI-assisted testing with safeguards and measurable validation.

Success measures include broader adoption of reliable automation, independent test data, and verified AI-assisted practices, alongside improved feedback speed, test stability, and release confidence.