Director - Enterprise Quality Methologies, Governance & Enablement

Humana; United States; Vor Ort; USD 180.000 - 240.000; Vollzeit

Zusammenfassung

Humana is seeking a Director to shape and govern enterprise quality methodologies across its software delivery ecosystem. You will define what good looks like, champion standardized testing practices, and drive adoption across SDLC, Agile, and DevOps frameworks to ensure responsible AI-enabled quality engineering.

You will partner with risk, compliance, legal, and data teams to align quality engineering standards with enterprise policies, while leading transformation programs that raise quality

Aufgaben

• Define and evolve the enterprise Quality Engineering strategy, standards, methodologies, and operating procedures across software delivery teams.
• Embed SDLC quality standards, policies, and guidance across planning, development, testing, release, and production validation.
• Lead AI-enabled quality engineering practices, governance, and transformation initiatives across Humana's software delivery ecosystem.

Jobbeschreibung

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The Director, Enterprise Quality Methodologies, Governance & Enablement will 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, DevOps governance routines, and responsible use of AI-enabled capabilities.

Key Responsibilities

Enterprise Quality Strategy, Policy, and Standards

• 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, DevOps 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.

AI-Enabled Quality Engineering and Transformation

• 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.

Governance, Compliance, and Quality Maturity

• 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 assess capability, prioritize improvement opportunities, and measure progress over time.
• Partner with Risk, Compliance, Cybersecurity, Audit, AI governance, and technology stakeholders to align quality engineering practices with enterprise controls, regulatory expectations, internal policies, and emerging AI governance standards.
• Develop governance routines, decision forums, scorecards, and reporting mechanisms to monitor adoption of quality standards and identify systemic risks.
• Use governance insights to drive continuous improvement across SDLC quality practices, DevOps controls, AI-enabled engineering practices, release quality, automation adoption, and production readiness.

Education, Enablement, and Transformation Support

• Lead organizational enablement programs that help teams adopt enterprise Quality Engineering standards, methodologies, tools, AI-enabled capabilities, and ways of working.
• Oversee Quality Engineering coaches who guide teams through quality planning, maturity assessments, platform onboarding, automation adoption, AI-assisted quality practices, and SDLC quality improvement.
• Partner with platform and engineering enablement teams to support onboarding and integration across enterprise engineeri