Director - Enterprise Quality Methologies, Governance & Enablement
Humana · New York, New York, United States · remote
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Humana is committed to helping people achieve their best health by delivering innovative technology solutions that power exceptional member, provider, and associate experiences.
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.
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.
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 engineering platforms, including Azure DevOps Services, GitHub Enterprise, LaunchDarkly, SonarQube, and related quality tools.
- Lead QE Community of Practice forums, learning initiatives, training programs, enablement content, and knowledge-sharing routines that strengthen enterprise quality capability and AI fluency.
- Support enterprise quality transformation initiatives by translating strategy into practical adoption models, playbooks, coaching plans, metrics, and change management approaches.
Quality Intelligence and Analytics
- Define and advance enterprise quality intelligence capabilities that provide actionable insight into engineering quality, SDLC compliance, quality maturity, release readiness, AI adoption, and production quality trends.
- Partner with data, platform, DevOps, AI, and engineering teams to develop dashboards and scorecards for quality standards adoption, automation coverage, code quality, deployment validation, performance testing, defect trends, governance compliance, and AI-enabled quality outcomes.
- Leverage quality and engineering metrics, including DevOps, DORA, pipeline, code quality, pull request, build, deployment, AI productivity, and compliance indicators, to guide improvement efforts.
- Translate quality data into executive insights, risk themes, maturity trends, and recommendations that improve technology delivery outcomes.
QE Audit and Control Support
- Lead Quality Engineering coordination for internal and external audits, including audit planning, evidence collection, control alignment, response coordination, and remediation tracking.
- Partner with Audit, Risk, Compliance, Cybersecurity, AI governance, Engineering, and platform teams to ensure Quality Engineering standards and evidence are aligned to applicable controls.
- Establish repeatable audit support processes, evidence repositories, ownership models, and reporting routines to improve audit readiness and reduce operational friction.
- Identify audit findings, control gaps, AI-related control considerations, and recurring evidence challenges; partner with stakeholders to drive corrective actions and sustainable improvements.
Leadership and Stakeholder Partnership
- Build, lead, and develop a high-performing team focused on quality methodology, governance, enablement, coaching, analytics, AI-enabled quality practices, and audit support.
- Influence leaders and teams across a matrixed enterprise environment to adopt consistent quality standards, measurable engineering practices, and responsible AI-enabled ways of working.
- Foster a culture of accountability, transparency, continuous improvement, learning, responsible innovation, and customer-centered quality.
- Serve as a trusted advisor to senior technology leaders on enterprise Quality Engineering maturity, AI-enabled transformation, risk, compliance, standards adoption, and transformation progress.
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