Senior Manager, AI & Data Governance

Weyerhaeuser•Seattle, WA

5d•$144,790 - $217,185

About The Position

Data and AI are foundational to how Weyerhaeuser operates, improves manufacturing performance, stewards timberlands, serves customers, manages risk, and enables better decisions. We are seeking a Senior Manager, AI & Data Governance to lead the enterprise AI and Data Governance capability within the AI organization. This leader will own the strategy, operating model, team, portfolio, and execution of governance practices that enable trusted business data products and responsible AI across the enterprise. This is a people and governance leadership role. Data Governance is an established discipline that must evolve to become more measurable, embedded in intake and delivery, and driven through business-owned data products. AI Governance operates in a fast-emerging technology, regulatory, and risk-management landscape and requires the ability to navigate ambiguity while enabling responsible innovation. The successful candidate will build stakeholder trust, establish repeatable governance mechanisms, and make governance practical and scalable. The role partners closely with the business stewards and owners, application and engineering. It also works across business leadership, IT Governance, Enterprise Architecture, Cybersecurity, Legal, Privacy, Risk, Finance and Audit, and Product. You have a high attention to detail, but are good at seeing the big picture, and aren’t afraid to think outside the box, and champion your ideas. You have experience articulating opportunity, as well as creating and successfully managing projects. You are effective at communicating timely and relevant information to business leaders and internal partners.

Requirements

10+ years of progressive experience in data governance, information management, data management, AI governance, risk management, analytics, or enterprise technology, including 7+ years leading teams, senior practitioners, or enterprise programs.
Demonstrated success establishing and operating enterprise Data Governance capabilities in a complex, multi-business environment, including ownership, stewardship, metadata, lineage, data quality, standards, and governance forums.
Experience working in an emerging AI, technology-risk, or regulatory environment, with the ability to evaluate new risks, establish proportionate controls, and enable responsible innovation amid ambiguity.
Strong knowledge of data-product governance and the ability to embed governance into intake, architecture, delivery, certification, adoption, measurement, and lifecycle management.
Experience partnering with architects, engineering teams, security, legal, privacy, risk, audit, product teams, and business leaders to translate policy and standards into practical operating mechanisms.
Experience governing enterprise data products, canonical data models, master or reference data, and federated business-domain ownership models.
Track record of hiring, coaching, and developing governance talent; leading through senior professionals and cross-functional stakeholders; and creating an inclusive, high-accountability culture.
Strong executive communication and stakeholder-management skills, including the ability to build trust, influence decisions across organizational boundaries, and connect governance investments to business outcomes.
Experience managing budgets, contractors, governance technologies, and strategic partners.
Experience with modern cloud data and AI platforms such as Snowflake, Microsoft Fabric, Purview, Azure services, AWS services, Power BI, SAP, and related engineering and AI, Data & MLOps practices.
Proven ability to influence technical strategy across multiple teams and organizations.
Bachelor's degree in Computer Science, Information Systems, Data Management, Engineering, Business, Risk Management, or a related discipline, or equivalent relevant experience.

Nice To Haves

Experience scaling an AI Governance, responsible AI program, model-risk framework, or AI lifecycle controls.
Experience assessing enterprise governance or discovery tooling, developing build-versus-buy recommendations, and translating capability gaps into prioritized investment roadmaps and funded implementation plans.
Experience with Microsoft Purview or comparable data catalog, metadata, lineage, quality, and stewardship platforms.
Experience implementing governance-by-design or governance-by-code through automated controls, workflow integration, continuous monitoring, or policy evidence.
Experience supporting manufacturing, supply chain, forestry, natural-resources, or other industrial and asset-intensive data domains.

Responsibilities

Lead, hire, coach, and retain a diverse team of governance analysts; set clear expectations, define career paths, and build succession and organizational capability across Data Governance, AI Governance, stewardship, and governance operations.
Lead effectively through governance analysts, engineering, architects, business owners, and stewards, translating enterprise strategy, governance standards, and risk objectives into durable operating practices and accountable outcomes.
Create a culture grounded in safety, inclusion, integrity, ownership, simplicity, learning, constructive challenge, and responsible innovation.
Build consistent governance mechanisms for intake, review, decision-making, documentation, issue resolution, stakeholder engagement, adoption, knowledge transfer, and continuous improvement across employees, contractors, and partners.
Define and execute a multi-year AI and Data Governance strategy and roadmap aligned to measurable business outcomes, enterprise architecture, Data & AI priorities, and risk-management objectives.
Own and evolve the enterprise Data Governance Charter, operating model, decision rights, stewardship framework, standards, and governance forums currently supporting enterprise data governance.
Own and evolve the AI Governance operating model as emerging technologies, regulations, enterprise risks, and industry practices change, while enabling responsible experimentation and adoption.
Drive the phased Governance roadmap. Assess AI coverage in the enterprise, establish classification at intake, and evaluate AI and data discovery capabilities. Own the governance capability roadmap and prioritization of governance solutions, including recommendations for build-versus-buy decisions related to governance, discovery, inventory, monitoring, and compliance capabilities.
Create a transparent service model for intake, discovery, prioritization, governance review, risk assessment, decisioning, escalation, monitoring, support, and lifecycle management across centralized and federated teams.
Maintain governance roadmaps, capacity plans, scorecards, operating reviews, maturity measures, and benefits tracking; reduce one-off governance through standardization, reuse, and automation while aligning work to enterprise priorities.
Govern internally developed, vendor-provided, and embedded AI capabilities, including machine learning, generative AI, AI agents, and emerging AI technologies across their lifecycle.
Define and maintain Responsible AI standards covering transparency, explainability, fairness, human oversight, reliability, privacy, security, and appropriate use, aligned to enterprise risk and business objectives.
Monitor evolving AI regulations, industry standards, and governance practices and translate them into practical, proportionate enterprise requirements that enable responsible innovation.
Partner with the AI Architect to translate the enterprise AI Governance operating model into practical policies, standards, risk tiers, lifecycle controls, review criteria, and technical guardrails, working alongside the AI Governance Council.
Partner with the Data Architect and data teams to govern canonical data models, business definitions, critical data elements, data contracts, authoritative sources, metadata, lineage, and reusable standards that accelerate trusted data-product delivery.
Embed Data Governance into business and technology intake so ownership, stewardship, quality, classification, architecture, lineage, and lifecycle expectations are defined early and carried through delivery and operation.
Advance a business data-product governance model with clear Data Owners, Data Stewards, product accountability, quality expectations, certification criteria, consumer feedback, adoption measures, and value realization.
Lead the governance platform roadmap, including Microsoft Purview and related capabilities for glossary, catalog, lineage, classification, stewardship workflows, controls, evidence, and reporting.
Partner with the AI Architect and AI Factory teams to define the utility, integration, prioritization, and sequencing of AI inventory, discovery, lifecycle-record, evidence, monitoring, and governance-automation capabilities so the operating model and AI Factory roadmap evolve together.
Partner with product, engineering, platform, and architecture teams to implement governance-by-design and governance-by-code patterns that automate controls, metadata capture, quality checks, policy evidence, and ongoing monitoring.
Build trust with business and technology stakeholders by making governance outcomes transparent, practical, measurable, and connected to data reliability, decision quality, operational performance, risk reduction, and business value.
Establish measurable outcomes for ownership and stewardship coverage, metadata completeness, data quality, lineage, policy compliance, data-product certification, AI risk reviews, control effectiveness, adoption, and stakeholder confidence.
Facilitate AI Governance Council operating mechanisms and partner with the Principal AI Architect, Legal, Cybersecurity, Data Governance, Privacy, Human Resources, Risk, and Audit to evaluate AI use cases, define Responsible AI requirements, govern AI risks and exceptions, and adapt controls as technologies, regulations, and enterprise risks evolve.
Facilitate Enterprise Data Council operating mechanisms and relevant governance functions to align Data and AI Governance with records, retention, privacy, legal, and regulatory requirements while maintaining clear accountability across governance disciplines.
Manage budgets, platform investments, and delivery trade-offs; communicate governance health, emerging risks, decisions, investment needs, and measurable business outcomes to technical and non-technical leaders.
Establish and oversee governance processes for identifying, documenting, escalating, remediating, and closing Data and AI Governance risks, exceptions, policy violations, and governance incidents.
Partners with Legal, Privacy, Cybersecurity, Risk, Audit, and business stakeholders to assess emerging risks, validate control effectiveness, and continuously improve governance practices.