Director Quality Engineering

Harnham; United States; On-site; USD 170,000 - 230,000; Full time

Job summary

Harnham is seeking a Director of Quality Engineering to transform QA from manual testing to automation-first practices across the organization. The role owns strategy, test automation, AI-enabled testing, quality metrics, and release readiness.

The leader partners with Engineering, Product, AI Foundation, Data & Analytics, Security, Privacy, Clinical, Operations, and vendors to build scalable quality practices that improve delivery speed, product reliability, and customer trust.

Qualifications

• Bachelor’s degree in a technical field.
• 15–18+ years in technology and software quality delivery.
• 7–10+ years leading QA/quality engineering teams.
• Experience modernizing QA practices and embedding automation.
• Experience partnering with product, engineering, security and data teams.

Responsibilities

• Define and lead enterprise quality engineering strategy and modernization roadmap.
• Establish enterprise quality standards across digital products and services.
• Embed automated testing into CI/CD pipelines and release processes.
• Lead AI-enabled testing capabilities, evaluation, and guardrails.
• Coach, develop, and scale QA and quality engineering talent.

Skills

Quality engineering leadership

Test automation strategy

AI-enabled testing

CI/CD integration

QA governance

Stakeholder management

Education

Bachelor’s degree in Computer Science, Engineering, or related

Tools

Automation frameworks

CI/CD tooling

Job description

Location Restrictions: Candidates cannot be based in California, New York, Hawaii, North Dakota, Oregon, Rhode Island, Washington, or Wyoming.

Work Authorization: U.S. Citizens or Green Card holders only.

The Director of Quality Engineering will lead the transformation of the organization’s quality assurance approach from primarily manual testing to a modern, automation-first quality engineering model. This leader owns quality engineering strategy, test automation, AI-enabled testing, quality metrics, release readiness, and the operating model for QA resources across the organization.

The role partners closely with Engineering, Product, Architecture, AI Foundation, Data & Analytics, Security, Privacy, Clinical, Operations, and vendor teams to build scalable quality practices that improve delivery speed, product reliability, and customer trust.

ESSENTIAL FUNCTIONS OF THE ROLE

Quality Engineering Strategy & Transformation

• Define and lead the enterprise quality engineering strategy, shifting from traditional manual QA toward automation-first, engineering-integrated, and AI-enabled quality practices.
• Establish a multi-year modernization roadmap covering test automation, tooling, metrics, delivery integration, talent development, and operating model changes.
• Set enterprise quality standards across digital products, application and platform teams, AI use cases, and shared technology services.
• Establish expectations for automated versus manual testing and drive practices that improve release confidence while reducing cycle time, rework, and late-stage testing.

Test Automation & AI-Enabled Testing

• Lead scalable test automation frameworks across API, UI, integration, regression, performance, accessibility, and end-to-end testing.
• Introduce AI-enabled testing capabilities including test generation and maintenance, defect analysis, intelligent regression selection, synthetic data, and QA productivity tools.
• Establish automation coverage targets, quality standards, and reporting for engineering and product leadership.
• Embed automated testing into CI/CD pipelines, release gates, and development workflows while evaluating emerging tools and AI capabilities.

AI Quality & Evaluation Partnership

• Partner with AI Architecture, AI Foundation, Engineering, Product, and Data teams to define quality practices for AI-enabled and agentic systems.
• Develop evaluation approaches covering expected behavior, acceptance criteria, guardrails, regression testing, hallucination/error detection, escalation patterns, and human-in-the-loop validation.
• Establish quality measures for accuracy, consistency, safety, traceability, source attribution, fallback behavior, and operational readiness.
• Integrate test automation, evaluation frameworks, monitoring, and feedback loops to enable AI use cases to scale safely.

Release Quality, Reliability & Operational Readiness

• Define release-readiness standards, quality gates, defect triage, regression expectations, test evidence requirements, and production validation practices.
• Establish clear quality metrics and release criteria for customer-facing and enterprise technology products.
• Partner with Engineering, DevSecOps, Operations, and Security to embed quality into CI/CD, deployment, monitoring, rollback, and incident response.
• Establish performance, reliability, accessibility, and security testing practices while driving improvements in defect leakage, test cycle time, automation coverage, and production stability.
• Oversee QA resources and establish a consistent operating model for quality engineering roles, responsibilities, standards, and engagement with engineering and product teams.
• Build, coach, and develop QA and quality engineering talent with an emphasis on automation, engineering partnership, and AI-enabled productivity.
• Define responsibilities across manual testing, quality engineering, test automation, product acceptance, engineering-owned quality, and AI evaluation.
• Identify capability gaps, upskilling needs, resource models, partner support, and hiring requirements.
• Build a quality engineering culture focused on speed, accountability, automation, proactive risk management, and measurable customer impact.

Cross-Functional Partnership & Governance

• Partner across Product, Engineering, Architecture, Security, Privacy, Compliance, Clinical, Data, Operations, and vendors to embed quality throughout delivery.
• Establish governance, metrics, standards, and playbooks that make quality expectations clear and actionable.
• Ensure practices align with enterprise technology standards, responsible AI expectations, data protection requirements, and regulated-industry obligations.
• Communicate quality strategy, modernization progress, delivery risks, tooling decisions, and performance metrics to leadership.
• Serve as the senior quality engineering advisor for major technology and AI-enabled product initiatives.

KEY SUCCESS FACTORS

• Proven ability to transform manual testing-heavy environments into automation-first, engineering-integrated quality practices.
• Deep expertise in test automation strategy, framework design, CI/CD integration, quality metrics, release gates, and scalable QA operating models.
• Strong understanding of agile delivery, DevSecOps, shift-left testing, automated regression, API/UI testing, performance testing, and production validation.
• Ability to apply AI and automation to improve QA productivity, coverage, cycle time, and release confidence.
• Strong understanding of AI quality challenges including evaluation, guardrails, traceability, safety, and regression risk.
• Demonstrated ability to influence engineering, product, architecture, operations, security, and business stakeholders around enterprise quality standards.
• Strong experience building, coaching, and upskilling quality engineering teams in complex enterprise environments.
• Ability to balance delivery speed with reliability, compliance, customer trust, and operational readiness in regulated or mission-critical environments.

MINIMUM REQUIREMENTS

Education

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related technical field.

Experience

• 15–18+ years of technology, software quality, quality engineering, test automation, software engineering, or related technology delivery experience.
• 7–10+ years of QA, quality engineering, automation, or engineering leadership experience.
• Demonstrated experience modernizing QA practices, scaling test automation, or transforming manual testing into automation-first quality engineering.
• Experience leading multidisciplinary teams including QA analysts, automation engineers, quality engineers, SDETs, performance testers, and/or partner resources.
• Experience embedding quality into agile delivery, CI/CD, DevSecOps, automated testing, release readiness, monitoring, and production validation.
• Experience partnering across product, engineering, architecture, security, privacy, operations, and business teams on complex technology initiatives.
• Experience with AI-enabled, data-intensive, customer-facing, or mission-critical products preferred.
• Experience in healthcare, life sciences, financial services, or another regulated/high-trust environment preferred.

Required Technical Expertise

• Strong foundation in software quality engineering, test automation, application delivery, API, integration and UI testing, and release validation.
• Experience with modern automation frameworks, test management, CI/CD integration, quality dashboards, defect analytics, and release-readiness reporting.
• Understanding of AI-enabled testing including test generation, intelligent test selection, AI-assisted defect analysis, synthetic data, and automation productivity tools.
• Familiarity with AI quality practices including evaluation frameworks, guardrail validation, traceability, monitoring, and regression testing.
• Ability to partner effectively with engineers, architects, platform teams, product managers, data teams, and operations teams.
• Strong understanding of security, privacy, compliance, reliability, accessibility, and customer trust for systems handling sensitive data.

PREFERRED QUALIFICATIONS

• Experience leading enterprise quality engineering, test automation, SDET, or QA transformation programs across multiple product teams or platforms.
• Experience with AI, GenAI, machine learning, conversational AI, automation, workflow systems, or decision-support products.
• Experience establishing quality engineering standards, tooling strategies, automation roadmaps, release-readiness playbooks, and measurable quality metrics.
• Experience using AI or automation to improve test creation, maintenance, analysis, coverage, regression selection, or QA productivity.
• Experience delivering technology in regulated industries involving sensitive data and auditability requirements.
• Experience working with external partners, vendors, systems integrators, or distributed engineering teams while maintaining internal quality ownership.
• Experience leading through organizational change, technical ambiguity, emerging technology, and evolving delivery practices.