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Lead Quality Engineer (Test Automation & Transformation)
8 West Consulting
Source: Headcount Labs · original listing ↗
Job data: Headcount Labs, ODbL 1.0. Job descriptions belong to their employers.
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About this role
About 8 West Consulting 8 West Consulting provides consultancy services and software solutions to clients across the US, Europe and the UK. We work in sectors including insurance, healthcare and retail, helping organisations tackle complex technology challenges and modernise critical platforms. The Opportunity We are looking for a hands-on Lead Quality Engineer to advance test automation and AI-enabled quality engineering across enterprise client teams. You will establish pragmatic technical direction, help several projects adopt repeatable practices and lead improvements to testing capability, while personally delivering automation, dependable test data and quality support on live engagements. This is not a strategy-only or people-management role. You will lead by building, demonstrating and coaching, helping teams move from manual-heavy testing towards reliable automation. The challenge is to make progress across established applications and different project needs, within enterprise controls and ongoing client commitments. Key Responsibilities • Lead cross-team transformation. Assess testing maturity across enterprise teams, prioritise common opportunities and lead phased adoption of automation, AI-assisted testing and better data practices. • Stay hands-on. Personally write and debug UI, API, integration and data tests; solve difficult automation problems, investigate defects and support releases. • Set technical direction. Define pragmatic quality engineering principles, review solutions and guide tool choices across projects while allowing for different client architectures and constraints. • Build shared test data capability. Design and implement reusable builders, fixtures, seeding and reset patterns for complex systems; enable independent tests across teams and environments. • Scale AI-enabled testing. Implement AI-assisted test design, case and script generation, synthetic data creation and failure analysis, then establish validation, safe-use practices and repeatable adoption. • Create reusable engineering practices. Establish lightweight patterns for frameworks, CI/CD, test reporting and governance; coach teams to apply them rather than create a central delivery bottleneck. • Develop engineering capability. Pair with testers and developers across projects, review implementations, mentor senior engineers and build practical training and adoption plans. • Influence senior stakeholders. Explain quality risks, investment choices, technical trade-offs and transformation progress to engineering leaders, delivery managers and client decision-makers. • Own outcomes across teams. Measure adoption and results across projects, including regression effort, test stability, independent data setup, AI effectiveness, feedback speed and release confidence. Essential Skills & Experience • Multi-team enterprise transformation. Has personally led substantial testing improvements across several teams, applications or workstreams in an enterprise environment, with clear evidence of adoption and delivery outcomes. • Recent hands-on engineering. Strong coding, debugging and automation skills, including direct work on production test suites in C#, TypeScript/JavaScript, Python, Java or equivalent. • AI implementation and adoption. Implemented AI-assisted testing on real enterprise projects and helped other engineers adopt it; can demonstrate verified test/data generation, practical safeguards and measured results. • Complex test data engineering. Has personally built reusable, isolated test datasets and shared patterns for relational or integrated systems using SQL, APIs, fixtures and builders; manages integrity, synthetic/masked data and reset. • Automation architecture and standards. Able to set sensible patterns across UI, API, integration, contract, data and regression tests, review designs and balance reuse with project-specific needs. • Microsoft-based environments. Practical experience testing .NET, SQL Server and/or Azure applications, including Azure DevOps or equivalent pipelines, Git and engineering workflows. • Tooling and delivery integration. Broad experience with Playwright, Selenium, Cypress, Postman or equivalents, plus CI/CD integration, test environments and flaky-test diagnosis. • Cross-project change leadership. Has built practical roadmaps, gained support across teams and delivered improvements incrementally despite competing deadlines and legacy constraints. • Executive communication and coaching. Can influence engineering and business leaders without formal authority, explain investment and quality trade-offs, and develop engineers through pairing and mentoring. Desirable Experience • Regulated enterprise environments. Insurance, healthcare, financial services or other controlled industries, with experience of auditability, privacy and sensitive test data. • Advanced AI quality practices. AI-assisted test-suite maintenance, agentic testing workflows, LLM evaluation, repeatable AI-feature test datasets or governance for AI testing tools. • Specialist testing. Performance/load, security, accessibility (WCAG), API contracts, complex integrations or data-heavy application testing. • Broader platforms and consultancy. Cloud-native, distributed, legacy or mobile systems; multi-client consulting, reusable accelerators or relevant testing and Azure certifications. Personal Profile You are a credible technical leader who still enjoys writing code and testing software. You can see patterns across different client environments without imposing unnecessary process. You solve difficult automation and data problems yourself, mentor others patiently and communicate clearly with senior stakeholders. You are realistic about AI, security and the pressure of live delivery. What Success Looks Like Multiple client teams adopt reliable automation, independent test data and verified AI-assisted testing practices that fit their delivery needs. Reusable approaches reduce duplicated effort, engineers gain confidence and the improvements are sustained beyond a single project. Progress is visible in adoption, feedback speed, test stability and release confidence, alongside dependable hands-on client delivery.