Senior Manager, AI & Data Governance

At Weyerhaeuser we are the worlds premier timberland and forest products company. Sustainability is the founding concept of our business and our values drive every decision to ensure we continue to lead the forestry industry in sustainability practices. And we know about sustainability we led it in the forestry industry when we planted our first seedling by hand in 1938. We recognize that our success is dependent on the success of our people. For over 125 years our Weyerhaeuser team has been making a difference in the world from the seedlings we plant to the forests and trees we nurture we ensure every acre is managed with diligence patience and pride. Thats the Weyerhaeuser way.

About the Role
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 arent 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.

Responsibilities:

People and Organizational Leadership

• 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.

Strategy Operating Model and Portfolio

• 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.

Platform Architecture and Data Products

• 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.

Reliability Governance and Business Partnership

• 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.

• 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.

Preferred not required:

• 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.

Education

• Bachelors degree in Computer Science Information Systems Data Management Engineering Business Risk Management or a related discipline or equivalent relevant experience.

What We Offer:

Compensation: This role is eligible for our annual merit-increase program and we are targeting a salary range of $85 based on your level of skills qualifications and experience. You will also be eligible for our Annual Incentive Program which offers a cash bonus targeting 25% of base pay. Potential plan funding may range from zero to two times that target.

Benefits: When you join our team you and your dependents will be offered coverage under our comprehensive employee benefits plan which includes medical dental vision short and long-term disability and life insurance. We offer a pre-tax Health Savings Account option which includes a company contribution. Other benefit options are also available such as voluntary Long-Term Care and Employee Assistance Programs. We also support personal volunteerism sponsor a host of diversity networks promote mentoring and provide training and development opportunities to help you chart your path to a fulfilling career. Retirement: Employees are able to enroll in our companys 401k plan which includes a paid company match in addition to our contribution equal to 5% of your eligible pay

Paid Time Off or Vacation: We provide eligible employees who are scheduled to work 25 hours or more per week with 3-weeks of paid vacation to use during your first year of addition after being employed for six months eligible employees begin to accrue vacation for future use. We also recognize eleven paid holidays per year providing a total of 88 holiday hours and paid parental leave for all full-time employees.

Weyerhaeuser is an equal opportunity employer. Inclusion is one of our five core values and we strive to maintain a culture where all our people feel a sense of belonging opportunity and shared purpose. We are committed to recruiting a diverse workforce and supporting an equitable and inclusive environment that inspires people of all backgrounds to join stay and thrive with our team.