Manager, Data Governance & Knowledge Management

Manager, Data Governance & Knowledge Management résultat

Cupertino, California, United States Corporate Functions

We are seeking a Manager of Data Governance & Knowledge Management to design,.white implement, and operate Finance’s data governance, data quality, and knowledge management capabilities. This role sits at the intersection of Finance, data engineering, and AI enablement, with a strong focus on execution and delivery. As part of the Finance Transformation organization, this role is responsible for ensuring Finance data and institutional knowledge are trusted, discoverable, explainable, and AI‑ready.

The manager will lead a small, dedicated team of engineers and partner closely with Finance Product, Engineering, IS&T, and finance domain experts. This is not a policy‑only governance role. It is a hands‑on leadership role focused on building the foundations that allow analytics, automation, and AI to scale safely and effectively within Finance.

This role ensures Finance can safely and confidently scale analytics and AI by building the data and knowledge foundations that make intelligence explainable, auditable, and trusted. It is a critical enabler of Finance Transformation and a key pillar of responsible AI adoption.

In this role, success means:

• Finance data is trusted, measurable, and consistently governed.
• Finance knowledge (rules, policies, KPIs, controls) is structured and reusable, not locked in documents or tribal knowledge.
• AI, analytics, and automation initiatives launch faster and with higher confidence because data and knowledge foundations are in place.
• Finance users trust AI outputs because they are explainable and correctable.
• Tangible progress is made toward AI readiness through delivered capabilities—not just frameworks.

• mplement and operate Finance‑aligned data governance and data quality frameworks, including ownership, definitions, quality rules, lineage, and issue resolution.
• Partner with IS&T to align Finance governance needs with enterprise platforms, certified data layers, access controls, and compliance requirements.
• Establish automated data quality monitoring for Finance‑critical datasets used in reporting, automation, and AI use cases.
• Develop data quality scorecards and metrics that demonstrate business impact and support explainability and trust.
• Ensure Finance data is traceable, documented, and auditable to support SOX, controls, and regulatory needs.
• Design and maintain Finance knowledge representations (semantic models, metadata, ontologies, knowledge graphs) that capture Finance logic, rules, KPIs, controls, and processes.
• Structure and prepare unstructured Finance data (policies, procedures, narratives, documentation) for AI consumption, including retrieval‑augmented generation and agent‑based workflows.
• Ensure Finance knowledge assets are discoverable, contextualized, and versioned, enabling explainable and trustworthy AI outputs.
• Partner with AI/ML and Automation teams to вкensure data and knowledge structures support including Explainability, Human‑in‑the‑loop validation, and Safe iteration and feedback‑driven improvement.
• Enable mechanisms for Finance users and SMEs to understand, validate, and correct AI‑ and analytics‑driven insights.
• Ensure outputs can be traced back to source data, business rules, and knowledge artifacts.
• Embed governance and explainability requirements into Finance analytics, automation, and AI delivery workflows from the start.
• Lead and mentor a small team of data engineers focused on Data quality automation, Metadata and lineage management, and Knowledge modeling and graph development.
• Translate Finance and Transformation priorities into clear, actionable technical work.
• Balance hands‑on contribution with people leadership, code/design review, and delivery accountability.
• Prioritize work based on Finance Transformation initiatives and AI‑readiness milestones.
• Partner closely with, Finance Product and Engineering teams, Program and Process leaders, IS&T data and platform teams.
• Work with Finance SMEs (FP&A, Accounting, Controllers, Audit, Risk) to accurately capture and formalize domain knowledge.
• Participate in enterprise…