Electronic Arts

EA Finance is transforming how data and AI power insights, automation, and decisions across the enterprise. As part of this multi-year Finance Transformation, we are establishing an integrated AI & Data Governance role that ensures Finance data, analytics, and intelligent systems are trusted, compliant, and responsibly managed.

The AI & Data Governance Manager will design, implement, and continuously improve the governance framework for Finance's data assets and AI solutions — ensuring that data is accurate and secure, and that AI models and agents operate safely, ethically, and efficiently. This role serves as the single point of accountability for Finance data and AI governance, partnering closely with Legal, Security, CTO D+I, and EAIT teams to embed governance into every phase of the data and AI lifecycle. You will report to the Senior Manager, FinTech Data & Insights.  This role follows a hybrid work model, requiring a minimum of three days per week in an EA office

Responsibilities & Outcomes

Data Governance

• Own and maintain the Finance data catalog and glossary
• Define and enforce data contracts, lineage, and SLAs for Gold tables and conformed dimensions across data domains.
• Implement data quality rules, monitors, and issue management workflows; maintain compliance dashboards and escalation processes.
• Oversee schema and metric change control, documentation, and deployment pipelines.
• Build and lead the Finance Data Steward network to promote shared accountability for data quality and metadata accuracy.

AI Governance

• Establish and manage the lifecycle for Finance AI assets — GPTs, copilots, and agents — including registration, validation, versioning, and decommissioning.
• Maintain a central catalog of Finance AI assets with ownership, accountability, and lifecycle tracking.
• Develop policies for responsible AI use: data sensitivity classifications, retrieval filters, output handling, and cost management.
• Oversee AI assurance reviews (accuracy, groundedness, fairness, bias) and coordinate remediation or retraining as needed.
• Partner with Security and Legal to enforce AI-related privacy

Security, Risk, and Compliance

• Apply RLS and OLS principles; manage secrets and credentials for both data and AI environments.
• Safeguard systems against prompt injection, unauthorized data access, and leakage of sensitive or confidential information.
• Coordinate with Enterprise Risk and Internal Audit for governance assurance cycles and observability reviews.

Policy, Framework, and Enablement

• Define, document, and publish unified Finance governance policies and standards for data and AI.
• Lead the Finance Data & AI Governance Council and ensure alignment with enterprise governance bodies.
• Deliver education and training programs to increase Finance literacy in data stewardship and responsible AI use.
• Develop runbooks for incident response, rollback, and recovery across data and AI systems.

Collaboration

• Partner with the FinTech Data & Insights, FinTech Systems, and Process Transformation hubs to embed governance early in design and delivery.
• Collaborate with CTO D+I for data pipeline enablement and EAIT for system integrations and infrastructure management.
• Support Finance leadership in prioritizing AI and data initiatives that align with transformation and compliance goals.

Required Qualifications

• 5+ years of combined experience in data governance, AI governance, analytics governance, or risk management in a regulated or public company.
• Hands-on experience with enterprise data tools (Microsoft Purview, Fabric Data Hub, or equivalent) and AI lifecycle management platforms.
• Knowledge of enterprise security, data privacy, and AI risk frameworks.
• Familiarity with concepts such as embeddings, retrieval grounding, prompt design, and model evaluation.
• Strong understanding of Finance data structures and processes (ERP, planning, consolidation, and analytics).
• Exposure to AI assurance, bias mitigation, model monitoring, and observability.
• Advanced degree in Information Systems, Data Science, or Business Analytics (or equivalent experience).