Hiring: Director Quality Engineering Company: Manulife Location: Boston, MA Job Posted Time: 2026-09-10 17:59:41 Employment Type: Not specified Target Skills & Keywords: Quality Engineering, Center of Excellence, SDLC, AI-Enabled Engineering, Automation Engineering, Test Automation, Generative AI, Self-Healing Automation, Intelligent Regression, Defect Triage, Synthetic Data, Predictive Quality Analytics, QE Maturity Models, Test Strategies, TDD, Change Failure Rate, Escaped Production Defects, Release Confidence, Automation ROI, Workforce Transformation, Governance, Tooling Standards, Dashboards & Scorecards Experience: - Proven leadership experience in enterprise Quality Engineering, including defining vision, strategy, standards, and operating models across engineering teams - Experience establishing and leading a Quality Engineering Center of Excellence, including governance forums, standards management, communities of practice, reusable assets, and capability development programs - Track record of defining QE maturity models, target-state capabilities, benchmarks, and multi-year transformation roadmaps - Experience developing and executing workforce transformation strategies, evolving traditional testing roles toward automation engineering and AI-enabled quality practices - Experience scaling QE innovations such as generative AI-assisted testing, self-healing automation, intelligent regression, defect triage, synthetic data, and predictive quality analytics - Experience influencing leaders, delivery partners, vendors, and cross-functional stakeholders to adopt QE standards and align on implementation approaches - Experience providing strategic guidance and technical oversight to platform engineering teams Required Skills: - Quality Engineering strategy, standards, and operating model definition - Quality Engineering Center of Excellence leadership and governance - QE maturity models, target-state capabilities, benchmarking, and transformation roadmapping - Modernization of testing practices to reduce manual effort and accelerate delivery - Workforce transformation toward automation engineering and AI-enabled quality practices - AI-assisted engineering across test generation and maintenance, defect analysis, code-quality validation, release-risk assessment, and autonomous testing - Quality frameworks for AI-enabled business applications, including model and prompt testing, bias validation, explainability, and production monitoring - Enterprise QE tooling standards, rationalization strategies, and adoption roadmaps - Test strategies, automation roadmaps, and quality measurement practices - Enterprise QE dashboards and scorecards measuring maturity, automation adoption, release readiness, defect trends, testing efficiency, and value realization - Embedding QE practices earlier in the SDLC, including discovery, intake, solution shaping, and delivery planning - QE integration into TDD targets with practical, measurable, outcome-focused goals - Metrics expertise: change failure rate, escaped production defects, release confidence, test automation reusability, AI testing adoption, QE maturity, test execution efficiency, and automation ROI - Stakeholder influence and cross-functional alignment - Evaluation of industry trends, emerging technologies, and AI-enabled testing capabilities Qualifications: - Not specified Compensation: - Not specified Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don’t miss this opportunity to join a forward-thinking team!