Director of Quality Engineering

Harnham; Dallas (TX); On-site; USD 180,000 - 230,000; Full time

Job summary

Harnham is seeking a Director, Quality Engineering to modernise software quality practices and drive an automation-first approach across multiple technology teams. You will define enterprise-wide quality strategy, establish scalable testing and automation capabilities, and partner with engineering, product, security, data, and operations to ensure reliable, well-governed software delivery.

The role accelerates delivery, strengthens operational readiness, and builds a culture of engineering

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline.
• 15+ years of experience in software engineering, software quality, quality engineering, test automation, or related technology functions.
• 7+ years of leadership experience managing QA, quality engineering, automation, or engineering teams.
• Experience embedding quality into Agile, DevOps, CI/CD, and release management practices.
• Experience with AI-enabled quality practices is highly valued.

Responsibilities

• Develop and execute an enterprise quality engineering strategy that advances automation and modern delivery practices.
• Create and manage a long-term roadmap covering automation tooling, testing frameworks, and governance.
• Define testing approaches including automated testing, manual validation, and engineering-owned quality practices.
• Lead automation framework development for API, UI, integration, and performance testing.
• Collaborate with cross-functional stakeholders to ensure quality requirements are identified early.

Skills

Leadership

Strategic thinking

Stakeholder engagement

Quality engineering

Automation strategy

Education

Bachelor's degree in CS/Engineering

Master's degree preferred

Job description

Director, Quality Engineering

Overview

A large enterprise organisation is seeking a senior quality engineering leader to modernise software quality practices and drive the transition from traditional testing approaches to an automation-first quality engineering model. This role is responsible for defining enterprise-wide quality strategy, establishing scalable testing and automation capabilities, advancing AI-enabled quality practices, and improving software delivery performance across multiple technology teams. The successful candidate will partner with engineering, product, architecture, security, data, operations, and business stakeholders to ensure technology solutions are delivered reliably, efficiently, and with appropriate controls.

The position plays a critical role in accelerating delivery velocity, improving product reliability, strengthening operational readiness, and fostering a culture of engineering ownership for quality.

Key Responsibilities

Quality Engineering Leadership & Transformation

• Develop and execute an enterprise quality engineering strategy that advances automation, engineering-led quality, and modern delivery practices.
• Create and manage a long-term roadmap covering automation tooling, testing frameworks, operating models, metrics, talent development, and process improvements.
• Establish organisation-wide quality standards across applications, platforms, digital products, and emerging technology solutions.
• Define appropriate testing approaches, including automated testing, manual validation, engineering-owned quality practices, and governance requirements.
• Drive improvements in release confidence, delivery speed, defect prevention, and overall quality outcomes.

Test Automation & Modern Quality Practices

• Lead the development and adoption of automation frameworks covering API, UI, integration, regression, accessibility, performance, and end-to-end testing.
• Implement AI-assisted testing capabilities such as automated test generation, intelligent test selection, defect analysis, test maintenance, and synthetic data support.
• Establish automation coverage goals, quality standards, and reporting frameworks.
• Embed automated testing into continuous integration and continuous delivery pipelines.
• Evaluate emerging tools and technologies that improve software quality and team productivity.

AI Product Quality & Evaluation

• Collaborate with technical and business stakeholders to define quality standards for AI-enabled products and intelligent workflows.
• Support the creation of evaluation frameworks that assess expected behaviours, acceptance criteria, guardrails, risk scenarios, and escalation paths.
• Ensure appropriate controls exist for accuracy, consistency, traceability, safety, monitoring, and operational readiness.
• Integrate testing, evaluation, monitoring, and feedback mechanisms into AI development lifecycles.
• Establish scalable approaches for validating AI-enabled solutions beyond manual review methods.

Release Quality & Operational Readiness

• Define release governance processes, quality gates, defect management standards, and production validation requirements.
• Establish quality metrics and release criteria for business-critical applications and technology services.
• Partner with delivery and operational teams to integrate quality into deployment, monitoring, rollback, and incident management processes.
• Oversee performance, accessibility, reliability, and non-functional testing practices.
• Drive continuous improvement in automation effectiveness, defect prevention, production stability, and release predictability.

Quality Organisation & Talent Development

• Lead and evolve the quality engineering operating model across a complex enterprise environment.
• Build, mentor, and develop quality engineering professionals with a focus on automation, engineering partnership, innovation, and continuous improvement.
• Clarify roles and responsibilities across manual testing, automated testing, quality engineering, product acceptance, and AI evaluation activities.
• Assess capability gaps, workforce planning requirements, upskilling opportunities, and partner support needs.
• Promote a culture of accountability, risk management, automation, and measurable customer outcomes.

Stakeholder Engagement & Governance

• Collaborate with cross-functional stakeholders to ensure quality requirements are identified early and incorporated throughout delivery.
• Establish governance forums, standards, metrics reviews, and operational playbooks.
• Ensure quality practices align with enterprise standards for security, privacy, compliance, accessibility, and risk management.
• Communicate quality strategy, programme progress, tooling decisions, risks, and performance metrics to senior leadership.
• Serve as a strategic advisor on major technology transformation and innovation initiatives.

Required Qualifications

Education

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related discipline.
• Master's degree preferred.

Experience

• 15+ years of experience in software engineering, software quality, quality engineering, test automation, or related technology functions.
• 7+ years of leadership experience managing QA, quality engineering, automation, or engineering teams.
• Demonstrated success transforming manual testing environments into automation-first quality engineering organisations.
• Experience leading multidisciplinary teams including quality engineers, automation engineers, SDETs, performance testers, analysts, and external partners.
• Proven experience embedding quality into Agile, DevOps, DevSecOps, CI/CD, release management, monitoring, and operational support practices.
• Strong track record partnering with engineering, product, architecture, security, operations, and business stakeholders.
• Experience supporting AI-enabled, data-intensive, customer-facing, or mission-critical systems is highly valued.

Technical Expertise

• Deep expertise in software quality engineering, automation strategy, and software delivery practices.
• Strong knowledge of API testing, integration testing, UI testing, performance testing, release validation, and test automation frameworks.
• Experience integrating automated testing into CI/CD pipelines and modern engineering workflows.
• Understanding of AI-assisted testing techniques and automation productivity tools.
• Familiarity with quality evaluation frameworks for AI-driven systems, including validation, monitoring, traceability, and risk management.
• Knowledge of security, privacy, compliance, accessibility, reliability, and governance considerations for enterprise technology environments.

Preferred Qualifications

• Experience leading enterprise-scale quality engineering transformation programmes.
• Experience establishing quality standards, automation strategies, testing roadmaps, quality metrics, and operational governance frameworks.
• Background supporting AI, machine learning, conversational AI, workflow automation, analytics, or data-driven applications.
• Experience working within regulated or highly governed industries.
• Experience managing vendor relationships, systems integrators, and distributed delivery teams.
• Demonstrated success leading through organisational change, emerging technologies, and evolving delivery models.