Director - Enterprise Quality Methologies, Governance & Enablement

Humana•New York, NY

4d•Remote

About The Position

The Director, Enterprise Quality Methodologies, Governance & Enablement leads the definition, governance, and enterprise adoption of quality engineering standards, methodologies, compliance alignment, quality intelligence, AI-enabled engineering practices, and transformation enablement. The role establishes expectations for quality engineering across Humana’s software delivery ecosystem and embeds consistent practices throughout the SDLC, Agile ways of working, engineering platforms, DevOps governance routines, and responsible use of AI-enabled capabilities. This leader partners with Quality Engineering, Enterprise Engineering Enablement, Agile Office, Product, Engineering, Architecture, Cybersecurity, Risk, Compliance, Audit, Data and AI teams, and segment technology teams. The role develops enterprise strategy, policies, standards, governance frameworks, maturity models, enablement programs, analytics, and audit support processes. It leads the integration and governance of AI-enabled quality practices, including responsible-use guidance, risk controls, human oversight, and measurement of adoption and value. The Director also oversees Quality Engineering coaches, supports platform onboarding and adoption, leads communities of practice and learning programs, and provides executive insight into quality maturity, compliance, risk, AI adoption, and transformation progress. The position is remote, with occasional travel to Humana offices for training or meetings. Home or hybrid employees must have internet service with at least 25 Mbps download and 10 Mbps upload and a dedicated workspace that protects member PHI/HIPAA information. Scheduled hours are 40 per week.

Requirements

At least 10 years of experience in quality engineering, software engineering, DevOps, technology governance, IT risk, compliance, technology enablement, AI-enabled engineering transformation, or related disciplines.
At least 5 years of leadership experience managing technical teams, enablement teams, governance functions, or enterprise-scale transformation capabilities.
Demonstrated experience defining and implementing software quality standards, methodologies, operating procedures, or SDLC governance practices.
Experience applying or governing AI-enabled capabilities in software engineering, quality engineering, test automation, analytics, productivity improvement, or technology transformation.
Experience with Agile delivery, DevOps practices, CI/CD pipelines, software engineering controls, release readiness, and production quality practices.
Experience developing maturity models, governance frameworks, scorecards, dashboards, and executive-level reporting.
Experience partnering with Risk, Compliance, Cybersecurity, Audit, Engineering, Product, AI/Data, and technology leadership stakeholders.
Strong understanding of quality engineering, including test automation, regression testing, deployment validation, code quality, performance testing, defect prevention, and responsible use of AI-assisted engineering tools.
Demonstrated ability to influence enterprise adoption of standards and best practices in a matrixed environment.
Strong executive communication, analytical, organizational, and change leadership skills.

Nice To Haves

Experience in healthcare, insurance, financial services, or another highly regulated environment.
Experience leading Quality Engineering enablement, Agile transformation, DevOps governance, engineering excellence, AI-enabled engineering transformation, or enterprise platform adoption.
Familiarity with Azure DevOps Services, GitHub Enterprise, LaunchDarkly, SonarQube, CI/CD templates, reusable workflows, feature flags, code quality tooling, DevOps governance practices, and AI-assisted engineering tools.
Experience with audit evidence management, control mapping, SDLC compliance, segregation of duties, access controls, vulnerability scanning, software delivery governance, and AI governance considerations.
Experience applying DORA, SPACE, code quality, pipeline, release, AI productivity, quality intelligence, and compliance metrics to improve engineering performance.
Experience building Communities of Practice, coaching models, training programs, learning journeys, AI literacy programs, or enterprise enablement frameworks.

Responsibilities

Define and evolve enterprise Quality Engineering strategy, standards, methodologies, operating procedures, and SDLC quality guidance.
Establish practical quality standards covering planning, development, testing, release, production validation, test automation, code quality, regression testing, deployment validation, performance testing, and release readiness.
Align Quality Engineering practices with Agile Office practices, DevOps governance, platform standards, enterprise delivery models, and responsible AI expectations.
Partner with engineering enablement teams to incorporate quality standards into tooling, pipelines, templates, reusable workflows, development practices, and AI-enabled engineering workflows.
Lead integration of AI-enabled quality engineering practices into methodologies, governance, coaching programs, and SDLC standards; define responsible-use guidance and partner on risk, privacy, and policy alignment.
Evaluate AI, machine learning, analytics, and intelligent automation opportunities to improve quality planning, coverage analysis, risk-based testing, defect prevention, release readiness, and production quality.
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 and a maturity model; develop governance routines, decision forums, scorecards, and reporting mechanisms.
Partner with Risk, Compliance, Cybersecurity, Audit, AI governance, and technology stakeholders to align practices with controls, regulatory expectations, internal policies, and emerging AI governance standards.
Use governance insights to improve SDLC quality, DevOps controls, AI-enabled engineering, release quality, automation adoption, and production readiness.
Lead organizational enablement programs and oversee Quality Engineering coaches supporting quality planning, maturity assessments, platform onboarding, automation adoption, AI-assisted quality, and SDLC improvements.
Support onboarding and integration across enterprise engineering platforms, including Azure DevOps Services, GitHub Enterprise, LaunchDarkly, SonarQube, and related quality tools.
Lead Quality Engineering Communities of Practice, learning initiatives, training programs, enablement content, and knowledge-sharing routines.
Translate quality transformation strategy into adoption models, playbooks, coaching plans, metrics, and change management approaches.
Define and advance quality intelligence capabilities, dashboards, and scorecards covering standards adoption, SDLC compliance, maturity, release readiness, AI adoption, and production quality trends.
Use DevOps, DORA, pipeline, code quality, pull request, build, deployment, AI productivity, and compliance indicators to guide improvement and provide executive insights, risk themes, maturity trends, and recommendations.
Lead Quality Engineering coordination for internal and external audits, including planning, evidence collection, control alignment, response coordination, and remediation tracking.
Establish repeatable audit support processes, evidence repositories, ownership models, and reporting routines; identify audit findings and control gaps and drive corrective actions with stakeholders.
Build, lead, and develop a high-performing team focused on methodology, governance, enablement, coaching, analytics, AI-enabled quality, and audit support.
Influence leaders and teams across a matrixed enterprise to adopt consistent quality standards and measurable, responsible engineering practices; advise senior technology leaders on maturity, transformation, risk, compliance, and standards adoption.