Senior Level Data Governance Lead Jobs
Senior level data governance lead jobs put experienced professionals in charge of enterprise data strategy, policy ownership, and the cross-functional programs that translate governance frameworks into organizational practice. Openings concentrate across Banking & Financial Services, Investment & Asset Management, and Technology & Software, with a mix of on-site, remote, and hybrid settings, and employers like Northwest Bank, Humana, and Fanduel hiring at this level now.
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Make your mark at Comcast - a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)
Job Summary
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. This leader will establish the standards and controls that protect the organization, define the architectures and platforms that enable scale, and develop data and AI products that deliver measurable value for customers, employees, and the business. The successful candidate will be equally comfortable setting enterprise direction, operating across organizational boundaries, and building a high-performing multidisciplinary team. They will bring together governance, architecture, and product management as complementary capabilities—not separate functions—and create an operating model that enables innovation while maintaining appropriate security, privacy, quality, and responsible-AI safeguards.
Job Description
Key Responsibilities:
Enterprise Leadership and Strategy
- Define and execute the enterprise strategy for data and AI governance, architecture, and products in alignment with business priorities and the broader data and AI strategy.
- Serve as a senior advisor to the Chief Data Officer, executive leadership, business leaders, 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, outcome-oriented portfolio with clear investment requirements, ownership, roadmaps, and measures of success.
- 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.
Data and AI Governance
- Establish and continuously improve an enterprise-wide governance framework covering 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 provides enterprise consistency while enabling business domains to move with appropriate autonomy.
- Partner with Legal, Privacy, Information Security, Compliance, Risk, Internal Audit, and business teams to embed regulatory and ethical requirements into everyday delivery.
- Oversee capabilities such as data ownership and stewardship, data quality, metadata and lineage, classification, access, retention, model governance, responsible AI, and third-party data and AI risk.
- Establish practical review and approval processes proportionate to the value, materiality, and risk of each use case.
- Develop metrics that demonstrate governance effectiveness, including improvements in data quality, policy adherence, control performance, model oversight, and speed to approved use.
Data and AI Architecture
- Define and maintain the 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 that 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.
Data and AI Product Leadership
- Establish and mature product management practices for enterprise data, analytics, AI, and shared-platform capabilities.
- Develop a portfolio of 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 where doing so improves scale, speed, and economics.
Organization and Talent Leadership
- 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 the organization’s 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.
Measures of Success
Success in this role will be demonstrated through outcomes such as:
- A clear and adopted enterprise strategy and operating model for data and AI governance, architecture, and products.
- Stronger alignment between business priorities and the data and AI investment portfolio.
- Increased adoption and measurable value realization from data and AI products.
- Improved data quality, trust, discoverability, lineage, and ownership.
- Responsible and timely delivery of AI capabilities with effective risk management and regulatory compliance.
- Greater reuse of shared data, models, platforms, architectural patterns, and product capabilities.
- Reduced duplication, technical debt, delivery friction, and unnecessary technology cost.
- Faster progression from business opportunity to governed, production-ready capability.
- A high-performing team with strong engagement, clear accountability, and a sustainable leadership pipeline.
Required Qualifications
- [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.
Preferred Qualifications
- 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.
Leadership Attributes
The successful candidate will demonstrate:
- Enterprise orientation: Optimizes for the organization as a whole rather than for an individual function or technology.
- Business and product mindset: Begins with user needs and business outcomes, not tools or technical novelty.
- Pragmatic governance: Creates clear, risk-based guardrails that build trust and enable responsible speed.
- Architectural judgment: Balances long-term coherence with near-term delivery and practical constraints.
- Strategic execution: Connects vision to decisions, investment, sequencing, accountability, and measurable results.
- Executive influence: Builds alignment through credibility, transparency, sound judgment, and constructive challenge.
- Talent leadership: Sets high standards while fostering trust, inclusion, learning, and psychological safety.
- Change leadership: Navigates ambiguity, competing priorities, and organizational resistance with resilience and discipline.
Disclaimer: This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.
Skills:
Organizational Leadership; Data Governance; AI Governance; AI Architecture; Product Management Leadership
Salary:
Pay Range: This job can be performed in District of Columbia, and Denver Campus with a Pay Range of $295,744.23 - $463,780.73
Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That’s why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality - to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.
Education
Master's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Relevant Work Experience
10 Years +
Make your mark at Comcast - a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.)
Job Summary
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. This leader will establish the standards and controls that protect the organization, define the architectures and platforms that enable scale, and develop data and AI products that deliver measurable value for customers, employees, and the business. The successful candidate will be equally comfortable setting enterprise direction, operating across organizational boundaries, and building a high-performing multidisciplinary team. They will bring together governance, architecture, and product management as complementary capabilities—not separate functions—and create an operating model that enables innovation while maintaining appropriate security, privacy, quality, and responsible-AI safeguards.
Job Description
Key Responsibilities:
Enterprise Leadership and Strategy
- Define and execute the enterprise strategy for data and AI governance, architecture, and products in alignment with business priorities and the broader data and AI strategy.
- Serve as a senior advisor to the Chief Data Officer, executive leadership, business leaders, 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, outcome-oriented portfolio with clear investment requirements, ownership, roadmaps, and measures of success.
- 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.
Data and AI Governance
- Establish and continuously improve an enterprise-wide governance framework covering 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 provides enterprise consistency while enabling business domains to move with appropriate autonomy.
- Partner with Legal, Privacy, Information Security, Compliance, Risk, Internal Audit, and business teams to embed regulatory and ethical requirements into everyday delivery.
- Oversee capabilities such as data ownership and stewardship, data quality, metadata and lineage, classification, access, retention, model governance, responsible AI, and third-party data and AI risk.
- Establish practical review and approval processes proportionate to the value, materiality, and risk of each use case.
- Develop metrics that demonstrate governance effectiveness, including improvements in data quality, policy adherence, control performance, model oversight, and speed to approved use.
Data and AI Architecture
- Define and maintain the 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 that 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.
Data and AI Product Leadership
- Establish and mature product management practices for enterprise data, analytics, AI, and shared-platform capabilities.
- Develop a portfolio of 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 where doing so improves scale, speed, and economics.
Organization and Talent Leadership
- 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 the organization’s 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.
Measures of Success
Success in this role will be demonstrated through outcomes such as:
- A clear and adopted enterprise strategy and operating model for data and AI governance, architecture, and products.
- Stronger alignment between business priorities and the data and AI investment portfolio.
- Increased adoption and measurable value realization from data and AI products.
- Improved data quality, trust, discoverability, lineage, and ownership.
- Responsible and timely delivery of AI capabilities with effective risk management and regulatory compliance.
- Greater reuse of shared data, models, platforms, architectural patterns, and product capabilities.
- Reduced duplication, technical debt, delivery friction, and unnecessary technology cost.
- Faster progression from business opportunity to governed, production-ready capability.
- A high-performing team with strong engagement, clear accountability, and a sustainable leadership pipeline.
Required Qualifications
- [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.
Preferred Qualifications
- 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.
Leadership Attributes
The successful candidate will demonstrate:
- Enterprise orientation: Optimizes for the organization as a whole rather than for an individual function or technology.
- Business and product mindset: Begins with user needs and business outcomes, not tools or technical novelty.
- Pragmatic governance: Creates clear, risk-based guardrails that build trust and enable responsible speed.
- Architectural judgment: Balances long-term coherence with near-term delivery and practical constraints.
- Strategic execution: Connects vision to decisions, investment, sequencing, accountability, and measurable results.
- Executive influence: Builds alignment through credibility, transparency, sound judgment, and constructive challenge.
- Talent leadership: Sets high standards while fostering trust, inclusion, learning, and psychological safety.
- Change leadership: Navigates ambiguity, competing priorities, and organizational resistance with resilience and discipline.
Disclaimer: This information has been designed to indicate the general nature and level of work performed by employees in this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications.
Comcast is an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.
Skills:
Organizational Leadership; Data Governance; AI Governance; AI Architecture; Product Management Leadership
Salary:
Pay Range: This job can be performed in District of Columbia, and Denver Campus with a Pay Range of $295,744.23 - $463,780.73
Comcast intends to offer the selected candidate base pay within this range, dependent on job-related, non-discriminatory factors such as experience. The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
The application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later.
Base pay is one part of the Total Rewards that Comcast provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non-sales positions are eligible for a Bonus. Additionally, Comcast provides best-in-class Benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That’s why we provide an array of options, expert guidance and always-on tools, that are personalized to meet the needs of your reality - to help support you physically, financially and emotionally through the big milestones and in your everyday life. Please visit the compensation and benefits summary on our careers site for more details.
Education
Master's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Relevant Work Experience
10 Years +
See All 24 Senior Level Data Governance Lead Jobs
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Find JobsSenior Level Data Governance Lead Job Market
Who's Hiring
Top Industries Hiring
- Banking & Financial Services
- Investment & Asset Management
- Technology & Software
- Retail
- Manufacturing
Senior Level Data Governance Lead Jobs: Frequently Asked Questions
How do I get a senior level data governance lead job?
Employers at this level look for candidates who have owned governance programs end to end, not just contributed to them. Demonstrating that you have defined data policies, led stewardship councils, and driven adoption across business units is what sets a senior candidate apart. Familiarity with frameworks like DAMA-DMBOK and the ability to align governance work to measurable business outcomes give you a clear edge.
Which companies hire senior level data governance leads?
Companies hiring senior level data governance leads right now include Northwest Bank, Humana, and Fanduel, based on current listings on Migrate Mate as of November 2026. Hiring at this level tends to come from large enterprises and regulated industries that have mature data programs and need experienced leaders to scale them.
Are there remote senior level data governance lead jobs?
Yes, though availability varies by employer and industry. About 20% of senior level data governance lead openings are remote or hybrid as of November 2026, reflecting the collaborative but portable nature of governance work. Roles tied to highly regulated environments or those requiring hands-on stakeholder alignment often prefer hybrid arrangements.
What makes a data governance lead role senior level?
Senior level data governance lead roles are defined by program ownership, strategic scope, and leadership responsibility. At this stage, you are setting governance direction, not executing tasks handed down by others. Expectations include designing enterprise-wide policies, influencing executive stakeholders, mentoring junior team members, and taking accountability for data quality and compliance outcomes across the organization.
Which industries hire the most senior level data governance leads?
Senior Level data governance lead roles concentrate in Banking & Financial Services, Investment & Asset Management, and Technology & Software, based on current listings on Migrate Mate as of November 2026. These sectors drive demand because they operate under strict regulatory requirements and manage high volumes of sensitive data, making experienced governance leadership a business-critical function rather than a supporting one.