Director - Enterprise Quality Methologies, Governance & Enablement

Humana•New York, NY

4d•$187,900 - $258,500•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 enterprise expectations for Quality Engineering and embeds consistent quality practices across the software development lifecycle (SDLC), Agile delivery, engineering platforms, DevOps governance, and responsible AI use. The Director partners with Quality Engineering, Engineering Enablement, Agile, Product, Engineering, Architecture, Cybersecurity, Risk, Compliance, Audit, Data and AI teams, and segment technology teams. The role oversees a team supporting quality methodology, governance, enablement, coaching, analytics, AI-enabled practices, and audit support; advises senior technology leaders; and drives measurable improvement in quality, delivery, governance, and organizational capability. This is a remote position with occasional travel to Humana offices for training or meetings. Home-based 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.

Requirements

10 or more years of experience in quality engineering, software engineering, DevOps, technology governance, IT risk, compliance, technology enablement, AI-enabled engineering transformation, or related disciplines.
5 or more 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 practices, 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.
Home or hybrid home/office work requires self-provided internet with at least 25 Mbps download and 10 Mbps upload and a dedicated workspace without ongoing interruptions to protect member PHI/HIPAA information.

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 policies and guidance.
Establish practical quality guidance covering planning, test automation, code quality, regression testing, deployment validation, performance testing, release readiness, production quality, and AI-assisted quality engineering.
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 reflect quality standards in enterprise tooling, pipelines, templates, reusable workflows, development practices, and AI-enabled workflows.
Lead integration of AI-enabled quality engineering into enterprise methodologies, governance, coaching, and SDLC quality standards.
Define responsible AI guidance for quality engineering, including test design and generation, defect and requirements analysis, code review support, test data analysis, automation, quality intelligence, and productivity improvement.
Work with Data, AI, Cybersecurity, Risk, Compliance, Legal, and Engineering stakeholders to align AI-enabled quality practices with enterprise policies, risk controls, privacy requirements, and responsible AI principles.
Evaluate generative AI, machine learning, analytics, and intelligent automation opportunities to improve quality planning, coverage, risk-based testing, defect prevention, release readiness, and production insights.
Establish governance and measurement 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 to assess capability, prioritize improvements, and measure progress.
Partner with Risk, Compliance, Cybersecurity, Audit, AI governance, and technology stakeholders to align practices with enterprise controls, regulatory expectations, internal policies, and AI governance standards.
Develop governance routines, decision forums, scorecards, and reporting mechanisms to monitor standards adoption and identify systemic risks.
Lead enablement programs that help teams adopt standards, methodologies, tools, AI-enabled capabilities, and ways of working; oversee Quality Engineering coaches supporting planning, maturity assessments, platform onboarding, automation, and SDLC improvement.
Partner with platform and engineering enablement teams on onboarding and integration across platforms including Azure DevOps Services, GitHub Enterprise, LaunchDarkly, SonarQube, and related quality tools.
Lead Quality Engineering Communities of Practice, learning initiatives, training, enablement content, and knowledge-sharing routines.
Translate quality transformation strategy into adoption models, playbooks, coaching plans, metrics, and change-management approaches.
Define and advance enterprise quality intelligence, including actionable insight into engineering quality, SDLC compliance, maturity, release readiness, AI adoption, and production quality trends.
Partner with data, platform, DevOps, AI, and engineering teams to develop dashboards and scorecards for standards adoption, automation coverage, code quality, deployment validation, performance testing, defect trends, governance compliance, and AI-enabled outcomes.
Use 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.
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 findings, control gaps, AI-related control considerations, and evidence challenges, and drive corrective action with stakeholders.
Build, lead, and develop a high-performing team focused on quality methodology, governance, enablement, coaching, analytics, AI-enabled practices, and audit support.
Influence leaders and teams across a matrixed enterprise to adopt consistent quality standards, measurable engineering practices, and responsible AI-enabled ways of working.
Foster accountability, transparency, continuous improvement, learning, responsible innovation, and customer-centered quality.
Serve as a trusted advisor to senior technology leaders on Quality Engineering maturity, AI-enabled transformation, risk, compliance, standards adoption, and transformation progress.