Director - Enterprise Quality Methologies, Governance & Enablement

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The Director, Enterprise Quality Methodologies, Governance & Enablementwill lead the definition, governance, and enterprise adoption of quality engineering standards, methodologies, compliance alignment, quality intelligence, AI-enabled engineering practices, and transformation enablement.

This leader will establish “what good looks like” for Quality Engineering across Humana’s software delivery ecosystem and ensure quality practices are consistently embedded across the SDLC, Agile ways of working, engineering platforms, Dev Ops governance routines, and responsible use of AI-enabled capabilities.

This role will partner closely with Quality Engineering, Enterprise Engineering Enablement, Agile Office, Product, Engineering, Architecture, Cybersecurity, Risk, Compliance, Audit, Data and AI teams, and segment technology teams to mature enterprise quality practices, strengthen governance frameworks, enable platform adoption, and improve quality outcomes across the organization.

Define and evolve the enterprise Quality Engineering strategy, standards, methodologies, and operating procedures across software delivery teams.

Establish SDLC quality standards, policies, and practical guidance that embed quality earlier and more consistently across planning, development, testing, release, and production validation.

Create and maintain enterprise engineering best practices, including “What Good Looks Like” guidance for quality planning, test automation, code quality, regression testing, deployment validation, performance testing, release readiness, production quality, and AI-assisted quality engineering practices.

Align Quality Engineering ways of working with Agile Office practices, Dev Ops governance, platform standards, enterprise delivery models, and responsible AI expectations.

Partner with engineering enablement teams to ensure quality standards are reflected in enterprise tooling, pipelines, templates, reusable workflows, development practices, and AI-enabled engineering workflows where appropriate.

Lead the integration of AI-enabled quality engineering practices into enterprise methodologies, governance models, coaching programs, and SDLC quality standards.

Define guidance for responsible use of AI in quality engineering, including test design, test generation, defect analysis, requirements analysis, code review support, test data analysis, automation acceleration, quality intelligence, and productivity improvement.

Partner with Data, AI, Cybersecurity, Risk, Compliance, Legal, and Engineering stakeholders to ensure AI-enabled quality practices align with enterprise policies, risk controls, privacy requirements, and responsible AI principles.

Evaluate opportunities to use generative AI, machine learning, analytics, and intelligent automation to improve quality planning, coverage analysis, risk-based testing, defect prevention, release readiness, and production quality insights.

Establish governance and measurement practices for AI-enabled quality capabilities, including adoption, value realization, risk management, human oversight, and continuous improvement.

Establish and maintain enterprise Quality Engineering governance frameworks that improve consistency, transparency, risk management, compliance alignment, and responsible AI adoption.

Define, implement, and mature a Quality Engineering maturity model that helps teams…