Executive Director, Data & AI Governance, Architecture and Products

Comcast • Philadelphia, PA

3d

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. The leader will establish standards and controls that protect the organization, define architectures and platforms that enable scale, and develop products that deliver measurable value for customers, employees, and the business. The role requires setting enterprise direction, working across organizational boundaries, and building a high-performing multidisciplinary team. It connects governance, architecture, and product management as complementary capabilities, with an operating model that enables innovation while maintaining security, privacy, quality, and responsible-AI safeguards.

The Executive Director will define and execute enterprise strategy; advise the Chief Data Officer and other senior stakeholders; establish an integrated operating model and outcome-oriented investment portfolio; and represent the organization in executive governance forums. The leader will oversee enterprise data and AI governance, including policies, decision rights, controls, stewardship, quality, metadata, access, model governance, responsible AI, and third-party risks. They will define target-state architectures and standards for enterprise data, analytics, machine learning, generative AI, and related platforms, while promoting interoperability, security, resilience, cost-effectiveness, and reuse.

The role will also establish product-management practices and a portfolio of reusable data and AI products, with clear users, outcomes, roadmaps, service expectations, product economics, and measures of value. The leader will recruit and develop a multidisciplinary organization, set its structure and ways of working, foster an inclusive and accountable culture, and manage its budget, workforce plan, vendors, and strategic partnerships. Success will be measured by adoption and value realization, improved data quality and trust, responsible delivery of AI, increased reuse, reduced duplication and technical debt, faster delivery of governed capabilities, and a strong leadership pipeline.

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.
• At least 10 years of relevant work experience.
• Demonstrates enterprise orientation, a business and product mindset, pragmatic governance, architectural judgment, strategic execution, executive influence, talent leadership, and change leadership.

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 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, architecture 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 business terms.
• Shape build, buy, partner, and reuse decisions across the data and AI ecosystem.
• Establish and improve enterprise governance for data, analytics, AI, and generative AI, including decision rights, accountabilities, policies, standards, controls, and escalation paths.
• Build a federated governance model that balances enterprise consistency with business-domain autonomy.
• Partner with Legal, Privacy, Information Security, Compliance, Risk, Internal Audit, and business teams to incorporate 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.
• Create practical review and approval processes proportionate to the value, materiality, and risk of use cases.
• Develop metrics for governance effectiveness, data quality, policy adherence, control performance, model oversight, and time 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 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.
• 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 with defined users, business outcomes, service expectations, and product economics.
• Create 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, 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 where this improves scale, speed, and economics.
• Recruit, develop, and lead a multidisciplinary organization, potentially including governance leaders, data stewards, architects, product executives and managers, responsible-AI specialists, and portfolio professionals.
• Design organizational roles, forums, and ways of working that connect governance, architecture, engineering, product, and business teams.
• Foster clarity, accountability, collaboration, inclusion, constructive challenge, and continuous learning.
• Develop succession plans and career paths and strengthen 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

• Eligible employees receive an array of benefits and personalized support options, with further details available in Comcast’s compensation and benefits summary.
• Most non-sales positions are eligible for a bonus.

What This Job Offers

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