Director Quality Engineering
Harnham
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Job summary
Harnham is seeking a Director of Quality Engineering to transform a manual QA environment into an automation-first organization. You will own the QA strategy, build automation capabilities, and lead a multi-team effort across API, UI, and end-to-end testing, embedding quality into CI/CD workflows.
The role requires 15+ years in QA/Quality Engineering and 7+ years in leadership, with a focus on modern testing architectures and AI-enabled testing approaches.
Qualifications
- 15+ years of experience across Quality Engineering, software quality, test automation, or related technology delivery.
- 7+ years of Quality Engineering, SDET, automation, QA, or engineering leadership.
- Proven experience transforming manual QA environments into automation-first Quality Engineering organizations.
- Experience building or substantially scaling test automation capabilities.
- Experience leading technical Quality Engineering, SDET, or engineering-adjacent teams.
- Deep understanding of modern test automation architecture and frameworks.
- Strong experience across API, UI, integration, regression, performance, and end-to-end testing.
Responsibilities
- Define and execute the enterprise Quality Engineering strategy.
- Lead the transformation from manual-heavy QA toward an automation-first engineering model.
- Build a multi-year roadmap across automation, tooling, talent, metrics, and operating model modernization.
- Establish and scale a dedicated SDET and test automation capability.
- Lead the existing QA organization through its transition toward a more technical, automation-oriented model.
- Design scalable automation frameworks across API, UI, integration, regression, performance, accessibility, and end-to-end testing.
- Embed automated testing into CI/CD pipelines, development workflows, and release gates.
- Establish shift-left testing practices and reduce reliance on late-stage manual validation.
- Define enterprise standards for quality gates, release readiness, regression testing, defect triage, and production validation.
- Establish meaningful metrics around automation coverage, defect leakage, test effectiveness, release predictability, and production stability.
- Introduce AI-enabled approaches to test generation, maintenance, defect analysis, regression selection, and synthetic data.
- Partner with Engineering, Product, Architecture, Security, Data, AI, and Operations teams.
- Establish evaluation and quality practices for AI-enabled and agentic systems
- Support validation of AI behavior, guardrails, traceability, safety, errors, fallback behavior, and human-in-the-loop workflows
- Build, coach, and develop technical Quality Engineering talent
Skills
Education
Tools
Job description
Occasional travel to Dallas, TX, potentially once per quarter
$245,000-$270,000 base + 20% annual bonus
U.S. Citizens and Green Card holders only — no sponsorship
California, New York, Hawaii, North Dakota, Oregon, Rhode Island, Washington, or Wyoming.
A large, mission-driven healthcare organization is investing heavily in modern engineering, automation, AI, and customer-facing digital experiences.
Operating at significant enterprise scale, the organization is accelerating its technology strategy and building modern capabilities across software engineering, data, AI, and digital products.
As engineering velocity increases, leadership is rethinking how quality is built into the software development lifecycle. The organization is now establishing a modern, automation-first Quality Engineering function capable of supporting both traditional software and the next generation of AI-enabled products.
The Director of Quality Engineering will lead the transformation of an established, predominantly manual QA environment into a modern, automation-first Quality Engineering organization.
This a true build opportunity.
Today, an existing QA team supports approximately 25 development teams, but there is no dedicated SDET function. The incoming Director will own the existing QA organization while building the technical capabilities, automation frameworks, SDET talent, standards, tooling, and operating model required for the future.
The immediate priority is establishing scalable deterministic automation across API, UI, integration, regression, performance, accessibility, and end-to-end testing while embedding quality directly into CI/CD and engineering workflows.
Longer term, the organization has an ambitious vision for AI-enabled and agentic testing - moving toward intelligent systems capable of generating tests, identifying defects, selecting regression coverage, and eventually executing increasingly complex testing workflows based on intent.
The successful candidate will be a builder who has transformed Quality Engineering before and is excited by the opportunity to create the strategy rather than inherit one.
- Define and execute the enterprise Quality Engineering strategy
- Lead the transformation from manual-heavy QA toward an automation-first engineering model
- Build a multi-year roadmap across automation, tooling, talent, metrics, and operating model modernization
- Establish and scale a dedicated SDET and test automation capability
- Lead the existing QA organization through its transition toward a more technical, automation-oriented model
- Design scalable automation frameworks across API, UI, integration, regression, performance, accessibility, and end-to-end testing
- Embed automated testing into CI/CD pipelines, development workflows, and release gates
- Establish shift-left testing practices and reduce reliance on late-stage manual validation
- Define enterprise standards for quality gates, release readiness, regression testing, defect triage, and production validation
- Establish meaningful metrics around automation coverage, defect leakage, test effectiveness, release predictability, and production stability
- Introduce AI-enabled approaches to test generation, maintenance, defect analysis, regression selection, and synthetic data
- Partner with Engineering, Product, Architecture, Security, Data, AI, and Operations teams
- Establish evaluation and quality practices for AI-enabled and agentic systems
- Support validation of AI behavior, guardrails, traceability, safety, errors, fallback behavior, and human-in-the-loop workflows
- Build, coach, and develop technical Quality Engineering talent
- 15+ years of experience across Quality Engineering, software quality, test automation, Software Engineering, or related technology delivery
- 7+ years of Quality Engineering, SDET, automation, QA, or engineering leadership
- Proven experience transforming manual QA environments into automation-first Quality Engineering organizations
- Experience building or substantially scaling test automation capabilities
- Experience leading technical Quality Engineering, SDET, or engineering-adjacent teams
- Deep understanding of modern test automation architecture and frameworks
- Strong experience across API, UI, integration, regression, performance, and end-to-end testing
- Experience embedding automated testing into CI/CD pipelines and release gates
- Strong understanding of shift-left testing and engineering-owned quality
- Experience defining quality gates, release-readiness standards, automation metrics, and testing strategy
- Strong understanding of modern software engineering, Agile, CI/CD, and DevSecOps
- Experience leading significant technical and organizational transformation
- Ability to influence Engineering, Product, Architecture, Security, Operations, and executive stakeholders
- Bachelor's degree in Computer Science, Engineering, Information Systems, or another related technical discipline
The strongest candidates will bring a point of view on where Quality Engineering is heading - not simply how testing has traditionally been done.
Experience or strong technical exposure in areas such as the following will stand out:
- AI-assisted test generation and maintenance
- Synthetic test data
- AI evaluation frameworks
- GenAI and conversational-system testing
- Expected-behavior and guardrail validation
- Agentic or intent-based testing
- Automated monitoring and feedback loops for AI systems
The longer-term objective is not simply to automate manual test cases. It is to create a Quality Engineering capability capable of supporting increasingly intelligent and autonomous software systems.
- Playwright, Maestro, or comparable modern automation frameworks
- Experience building an SDET organization from the ground up
- Experience transforming an established manual QA workforce
- AI, GenAI, machine learning, or agentic-system testing experience
- Experience with customer-facing or mission-critical digital products
- Healthcare or health technology experience
- Financial services, life sciences, or another regulated/high-trust industry
- Experience managing vendor, partner, or distributed QA resources
- Master's degree in a relevant technical discipline
Healthcare experience is valuable but is not the primary requirement for this search. Deep automation expertise and experience building technical Quality Engineering capabilities are significantly more important.
- Build an enterprise Quality Engineering capability from a near-blank slate
- Define the automation strategy, frameworks, tooling, standards, and operating model
- Establish a dedicated SDET capability
- Transform an established manual QA environment rather than simply optimize an already mature automation program
- Own both the technical strategy and organizational evolution
- Build automation directly into modern engineering and CI/CD workflows
- Help define how Quality Engineering evolves for GenAI and agentic systems
- Work toward an ambitious longer-term vision for intent-based and agentic testing
- Partner directly with senior Engineering, Product, Architecture, Data, and AI leaders
- Work on customer-facing technology where quality and reliability have meaningful real-world impact
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