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 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.

Required Qualifications:

• 7+ years of experience in software development, with a focus on Quality Engineering, transformation, test automation, engineering excellence, or a comparable technology leadership role.
• Experience applying AI-enabled testing, automation, analytics, and emerging technologies to improve quality and productivity and increase autonomy in the SDLC.
• Experience driving AI SDLC adoption, change enablement, and measurable productivity improvements across engineering or QE teams.
• Experience with generative AI-assisted testing, intelligent automation, quality analytics, defect prediction, or test optimization.
• Experience defining, implementing, and scaling QE standards, best practices, and governance models across teams.
• Demonstrated influencing, facilitation, and stakeholder-management skills, with the ability to build consensus among senior leaders, delivery teams, vendors, and business partners.
• Experience with third-party QE resource governance, vendor engagement, and capacity planning.
• Clear communication, presentation, documentation, coaching, and mentoring skills, including the ability to translate technical concepts into practical guidance for varied audiences.
• Experience leading complex, cross-functional initiatives, supporting adoption of new practices, and managing change across multiple teams or portfolios.
• Experience creating test strategies, automation roadmaps, reusable test assets, business use-case coverage, and risk-based testing approaches.

Preferred Qualifications:

• Strategic QE leadership and community enablement.
• Influencing, stakeholder management, and change leadership.
• Coaching, mentoring, facilitation, and knowledge sharing.
• Quality Engineering standards, best practices, and governance.
• Risk-based testing, regression optimization, and quality metrics.
• AI-enabled testing, machine learning model testing, and emerging quality practices.
• AI SDLC adoption, responsible AI governance, and QE maturity models.
• QE innovation, AI use-case scaling, and adoption metrics.
• CI/CD, DevSecOps, Agile delivery, and shift-left quality practices.

When you join our team: 

• We’ll empower you to learn and grow the career you want.  
• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.  
• As part of our global team, we’ll support you in shaping the future you want to see. 

The role being advertised is an existing vacancy.

About Manulife and John Hancock

Manulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html.

Manulife is an Equal Opportunity Employer

At Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy-related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.

It is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. To request a reasonable accommodation in the application process, contact [email protected].

Referenced Salary Location

Salary range is expected to be between

Employees also have the opportunity to participate in incentive programs and earn incentive compensation tied to business and individual performance. The actual salary will vary depending on local market conditions, geography and relevant job-related factors such as knowledge, skills, qualifications, experience, and education/training. If you are applying for this role outside of the primary location, please contact [email protected] for the salary range for your location.

Manulife/John Hancock offers eligible employees a wide array of customizable benefits, including health, dental, mental health, vision, short- and long-term disability, life and AD&D insurance coverage, adoption/surrogacy and wellness benefits, and employee/family assistance plans. We also offer eligible employees various retirement savings plans (including pension/401(k) savings plans and a global share ownership plan with employer matching contributions) and financial education and counseling resources. Our generous paid time off program in the U.S. includes up to 11 paid holidays, 3 personal days, 150 hours of vacation, and 40 hours of sick time (or more where required by law) each year, and we offer the full range of statutory leaves of absence.

We use data and analytics technologies, such as artificial intelligence (AI), and automated processing tools, to analyze and process the information you provide to us or third parties in the application process. For more information, please refer to our personal information collection statement.

Know Your Rights I Family & Medical Leave I Employee Polygraph Protection I Right to Work I E-Verify