Director Quality Engineering

The Director Quality Engineering advances enterprise quality engineering maturity by setting standards, leading a Quality Engineering Center of Excellence, embedding quality earlier in the software development life cycle (SDLC), and accelerating the responsible adoption of AI-enabled engineering practices. This leader will guide workforce transformation toward automation engineering, AI-enabled quality practices, and shared engineering ownership of quality outcomes.

• Define the vision, strategy, standards, and operating model for consistent Quality Engineering (QE) practices across teams.
• Establish and lead the Quality Engineering Center of Excellence, including governance forums, standards management, communities of practice, reusable assets, and capability development programs.
• Define QE maturity models, target-state capabilities, benchmarks, and multi-year transformation roadmaps across engineering teams.
• Modernize testing practices to improve quality outcomes, reduce manual effort, and accelerate delivery.
• Develop and execute a workforce transformation strategy that evolves traditional testing roles toward automation engineering, AI-enabled quality practices, and shared engineering ownership of quality.
• Expand AI-assisted engineering capabilities across test generation and maintenance, defect analysis, code-quality validation, release-risk assessment, and autonomous testing.
• Define quality frameworks for AI-enabled business applications, including model and prompt testing, bias validation, explainability, and ongoing production monitoring.
• Evaluate industry trends, emerging technologies, and AI-enabled testing capabilities to improve speed, coverage, reliability, scalability, and productivity.
• Define enterprise QE tooling standards, rationalization strategies, and adoption roadmaps to reduce fragmentation, increase reusability, and maximize the value of QE investments.
• Scale QE innovations such as generative AI-assisted testing, self-healing automation, intelligent regression, defect triage, synthetic data, and predictive quality analytics.
• Influence leaders, delivery partners, vendors, and cross-functional stakeholders to adopt QE standards, prioritize quality improvements, and align on implementation approaches.
• Provide strategic guidance and technical oversight to platform engineering teams, supporting scalable QE practices, modern testing architectures, and enterprise-aligned standards.
• Build alignment through forums, communities of practice, knowledge-sharing activities, and reusable assets that support consistent adoption of modern testing capabilities.
• Define test strategies, automation roadmaps, and quality measurement practices that improve coverage, reduce regression cycle time, and strengthen release confidence.
• Establish enterprise QE dashboards and scorecards that measure maturity, automation adoption, release readiness, defect trends, testing efficiency, and value realization, while maintaining clear accountability with platform engineering leaders.
• Embed QE practices earlier in the SDLC, including discovery, intake, solution shaping, and delivery planning.
• Champion QE integration into TDD targets, ensuring goals are practical, measurable, and focused on outcomes.
• Use relevant measures such as change failure rate, escaped production defects, release confidence, test automation reusability, AI testing adoption, QE maturity, test execution efficiency, and automation return on investment.
• Promote a culture of engineering excellence, continuous learning, knowledge sharing, and accountability for quality outcomes.

• Define the AI-led testing strategy, adoption roadmap, maturity model, and governance required to move QE from experimentation to scalable adoption.
• Establish governance, controls, and monitoring for AI capabilities in alignment with regulatory requirements, model risk management standards, privacy obligations, and responsible AI principles.
• Set standards, guardrails, quality gates, reusable assets, and adoption measures for AI-assisted testing.
• Scale successful AI-enabled QE use cases to improve coverage, reliability, cycle time, release confidence, and cost efficiency.
• Maintain awareness of emerging AI technologies, industry trends, and vendor capabilities, translating relevant opportunities into practical business value.

• 7+ years of experience in software development, with a focus on Quality Engineering, transformation, test…