Executive Director, Data & AI Governance, Architecture and Products

Comcast Resume • Washington, DC

1d • $295,744 - $463,781

About The Position

Comcast is seeking an enterprise leader to build and lead an integrated organization spanning Data and AI Governance, Data and AI Architecture, and Data and AI Products. The Executive Director will translate enterprise strategy into a coherent portfolio of trusted, scalable, and high-value data and AI capabilities, establishing standards and controls, defining architectures and platforms, and developing products that deliver measurable value for customers, employees, and the business.

The role sets enterprise direction and works across organizational boundaries while building a high-performing multidisciplinary team. It brings governance, architecture, and product management together as complementary capabilities, creating an operating model that enables innovation while maintaining appropriate security, privacy, quality, and responsible-AI safeguards.

The Executive Director will define enterprise strategy and operating models; oversee governance frameworks and controls; set target-state architecture and technical standards; mature product management and reusable data and AI products; and recruit, develop, and lead a multidisciplinary organization. The role also manages budgets, workforce plans, vendor relationships, and strategic partnerships, and advises senior leaders on opportunities, investments, dependencies, and risks.

Success includes adoption of an enterprise strategy and operating model, stronger alignment of investments to business priorities, increased product adoption and value realization, improved data quality and trust, responsible and timely AI delivery, greater reuse, reduced duplication and technical debt, faster delivery of governed capabilities, and a high-performing team.

Requirements

• 12–15+ years of progressive leadership experience across data, analytics, AI, technology, digital product management, or related disciplines.
• Significant experience leading multiple functions at enterprise scale, ideally including governance, architecture, product management, data platforms, or AI.
• Demonstrated success translating enterprise strategy into operating models, investment portfolios, roadmaps, and measurable business outcomes.
• Strong understanding of modern data and AI architectures, platforms, integration patterns, cloud ecosystems, analytics, machine learning, generative AI, and operationalization practices.
• Experience establishing data governance, responsible-AI, model-governance, privacy, security, and risk-management capabilities in a complex environment.
• Proven ability to develop and manage data, platform, analytics, or AI capabilities as products with defined users, roadmaps, service levels, adoption goals, and value measures.
• Experience influencing senior executives and aligning stakeholders across business, technology, legal, security, risk, and compliance functions.
• Demonstrated success building, developing, and retaining diverse, high-performing teams.
• Strong financial and portfolio-management capabilities, including investment prioritization, workforce planning, vendor management, and value realization.
• Bachelor’s degree in business, technology, computer science, engineering, data science, or a related field, or equivalent professional experience.
• Enterprise orientation, optimizing for the organization as a whole rather than an individual function or technology.
• Business and product mindset, beginning with user needs and business outcomes rather than tools or technical novelty.
• Pragmatic governance skills, creating clear, risk-based guardrails that build trust and enable responsible speed.
• Architectural judgment to balance long-term coherence with near-term delivery and practical constraints.
• Strategic execution skills connecting vision to decisions, investment, sequencing, accountability, and measurable results.
• Executive influence grounded in credibility, transparency, sound judgment, and constructive challenge.
• Talent leadership that sets high standards while fostering trust, inclusion, learning, and psychological safety.
• Change leadership to navigate ambiguity, competing priorities, and organizational resistance with resilience and discipline.

Nice To Haves

• Experience working in a highly regulated, global, or operationally complex organization.
• Experience implementing federated data governance, domain-oriented data ownership, data-product models, or enterprise AI governance.
• Familiarity with applicable data, privacy, AI, records-management, and industry-specific regulatory requirements.
• Experience leading enterprise transformation and organizational change across both business and technology functions.
• Advanced degree in a relevant discipline.
• Relevant credentials in enterprise architecture, data management, product management, cloud technology, risk, privacy, or AI governance.

Responsibilities

• Define and execute enterprise strategy for data and AI governance, architecture, and products in alignment with business priorities and broader data and AI strategy.
• Advise the Chief Data Officer, executive leadership, business and technology leaders, and risk partners on data and AI opportunities, investments, dependencies, and risks.
• Establish an integrated operating model connecting business priorities, governance decisions, architectural standards, platform capabilities, and product delivery.
• Translate strategic priorities into a sequenced portfolio with investment requirements, ownership, roadmaps, and success measures.
• Represent the organization in executive governance forums and communicate complex data and AI topics in clear business terms.
• Shape build, buy, partner, and reuse decisions across the data and AI ecosystem.
• Establish and continuously improve an enterprise-wide governance framework for data, analytics, AI, and generative AI.
• Define decision rights, accountabilities, policies, standards, controls, and escalation paths across the data and AI lifecycle.
• Build a federated governance model that maintains enterprise consistency while enabling appropriate autonomy for business domains.
• Partner with Legal, Privacy, Information Security, Compliance, Risk, Internal Audit, and business teams to embed regulatory and ethical requirements into delivery.
• Oversee data ownership and stewardship, data quality, metadata and lineage, classification, access, retention, model governance, responsible AI, and third-party data and AI risk capabilities.
• Establish practical review and approval processes proportionate to each use case’s value, materiality, and risk.
• Develop metrics for governance effectiveness, including data quality, policy adherence, control performance, model oversight, and speed to approved use.
• Define and maintain target-state architecture for enterprise data, analytics, machine learning, generative AI, and related platforms.
• Establish architecture principles, reference patterns, technical standards, reusable components, and lifecycle-management practices.
• Ensure architectures are scalable, interoperable, secure, resilient, cost-effective, and aligned with enterprise technology strategy.
• Guide decisions involving cloud and hybrid platforms, data integration, APIs, lakehouse and warehouse technologies, semantic layers, master and reference data, model platforms, AI agents, and MLOps/LLMOps.
• Reduce unnecessary technology fragmentation and technical debt while improving reuse, portability, and time to delivery.
• Partner with engineering, cybersecurity, infrastructure, application, and business architecture leaders to ensure end-to-end alignment.
• Chair or lead relevant architecture councils and provide timely, pragmatic guidance to delivery teams.
• Establish and mature product management practices for enterprise data, analytics, AI, and shared-platform capabilities.
• Develop reusable data and AI products tied to clearly defined users, business outcomes, service expectations, and product economics.
• Create transparent product roadmaps informed by enterprise strategy, user research, value potential, risk, feasibility, and organizational readiness.
• Ensure product teams own the full lifecycle from discovery and prioritization through delivery, adoption, measurement, maintenance, and retirement.
• Define product health and value metrics, including adoption, customer satisfaction, business impact, reliability, quality, cost, and speed to insight or action.
• Partner with business leaders to identify high-value opportunities and establish shared accountability for adoption and realized benefits.
• Promote reusable products and platform capabilities over one-off solutions when this improves scale, speed, and economics.
• Recruit, develop, and lead a multidisciplinary organization that may include governance leaders, data stewards, architects, product executives, product managers, responsible-AI specialists, and portfolio professionals.
• Design the organization, roles, forums, and ways of working needed to connect governance, architecture, engineering, product, and business teams.
• Create a culture of clarity, accountability, collaboration, inclusion, constructive challenge, and continuous learning.
• Develop succession plans and career paths while strengthening enterprise data, AI, architecture, and product-management capabilities.
• Set clear objectives and empower teams to make decisions within well-defined guardrails.
• Manage the organization’s budget, workforce plan, vendor relationships, and strategic partnerships.

Benefits

• Comcast benefits for eligible employees, with details available in the compensation and benefits summary on the careers site.
• Most non-sales positions are eligible for a bonus.

What This Job Offers

• Job Type: Full-time
• Career Level: Executive