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.

Position Responsibilities: 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. AI, Automation & Intelligent Decisioning 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 prin