Manager Data Governance

Department Overview

As a Manager within McDonald's Responsible AI (RAI) team, reporting to the Senior Manager, Responsible AI (AI Risk & Governance), you will serve as a critical bridge between Responsible AI strategy and execution, translating governance requirements into practical processes, platform capabilities, and operational ways of working. The position is responsible for driving day-to-day execution of AI governance initiatives, improving governance workflows, establishing platform integrations, and ensuring AI assets are governed consistently across the enterprise.You will partner closely with Data Governance, Data Science, AI Platform Engineering, Security, Legal, and Business stakeholders to operationalize governance requirements within AI development ecosystems and enterprise governance platforms. Focus will be placed on AI inventory management, model governance, agent governance, platform integrations, and lifecycle management capabilities.This role combines governance platform expertise, technical execution, process optimization, stakeholder management, and business judgment to help scale McDonald's Responsible AI program while enabling innovation across the enterprise.

Duties

Who we’re looking for:

We are seeking an experienced governance and technology leader who can bridge business governance requirements with technical implementation.The ideal candidate combines expertise in governance platforms, metadata management, AI/ML ecosystems, and enterprise operating models. They are equally comfortable discussing governance controls with Legal and Risk teams as they are discussing integrations, metadata, APIs, model registries, and platform architecture with engineering teams.

Primary Responsibilities:

AI Governance Platform Ownership:Own the day-to-day operation and enhancement of McDonald's AI Governance platform capabilities, workflows, and automations, including: AI Use Case Registry, AI Model Inventory, Agentic Inventory and Governance
AI/ML Platform Integration & Connectivity: Lead technical implementation of integrations between governance platforms and enterprise AI/ML ecosystems, such as Vertex AI
Agentic AI Governance: Defining governance requirements, inventory processes, and guardrails implementation and reporting for enterprise agentic platform (e.g. Gemini, Copilot, other agent-builder platforms)
Enterprise Cross-functional Risk & Governance Alignment: Partner closely with our key business and technology stakeholders (AI Legal + Security + Data Science + AI Platform Engineering + GCP Platform + others) to align governance processes, controls, and platform capabilities. Click to add description here.

Qualifications

Key Qualifications:

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Analytics, or related field.
• 8+ years of experience in  AI governance, model governance, or Responsible AI programs (or related exposure via strong data governance foundations)
• Experience implementing or operating data and AI governance platforms (e.g. Collibra, Informatica) including: platform integrations, APIs, and metadata ingestion solutions
• Strong understanding of enterprise metadata management and inventory management concepts
• Experience working with cloud platforms such as Google Cloud Platform, Azure, or AWS
• Experience working with AI, machine learning, advanced analytics, or data science teams
• Experience designing or improving enterprise governance processes
• Strong analytical, program management, and stakeholder management skillsAbility to work effectively across business, risk, and technical teams

Preferred Qualifications:

• Hands-on experience with Collibra
• Experience with Vertex AI, Databricks, Azure AI, MLflow, or similar AI/ML ecosystems
• Knowledge of AI regulations and frameworks (e.g., EU AI Act, NIST AI RMF, ISO 42001)
• Familiarity with agent-based AI and enterprise AI assistant ecosystems (e.g. Gemini, Copilot)
• Experience working within enterprise risk, governance, compliance, or control environments
• Deep knowledge of software development lifecycle, DevOps, and MLOps concepts