Data Governance & Enablement Manager

Data Governance & AI Enablement Manager

SUMMARY
BankUnited's Technology Data Management team is digitally redefining the Bank with data and AI by enabling teams across the organization to align their growing data assets and analytics capabilities across various sources and platforms to inform and transform business outcomes.

Enabling this transformation requires the creation, management, sharing, and consumption of data and AI-driven insights to generate measurable business value. We are seeking a Data Governance & AI Enablement Manager to play a key role in developing and advancing the Bank’s data and AI governance strategy and operating model.

 

This role is responsible for establishing, operationalizing, and continuously maturing enterprise data governance capabilities, while also enabling responsible and scalable AI adoption. The position will leverage industry-standard frameworks (e.g., DCAM, DAMA-DMBOK) to assess and improve the Bank’s data management maturity, ensuring strong control, transparency, and value realization across the full data and AI lifecycle.

The role focuses on treating data as a strategic enterprise asset—emphasizing ownership, quality, traceability, control, and value—and ensuring that AI/analytics initiatives are supported by trusted, well-governed data and aligned with risk and regulatory expectations.

 

KEY RESPONSIBILITIES

Framework Design & Strategy

• Lead the design and evolution of the Data & AI Governance framework to support business value realization and regulatory alignment
• Collaborate with business and technology stakeholders to establish strong data ownership, stewardship, and accountability
• Align governance frameworks with industry standards (e.g., DCAM, DAMA-DMBOK, COBIT) Define governance capabilities, control objectives, and measurable outcomes across data and AI domains Governance Implementation & Operations
  Implement and operationalize governance frameworks across business, data, and technology teams
• Drive stakeholder engagement, adoption, and adherence to governance policies and standards
• Support governance committees and data/AI communities as key forums for alignment and decision-making
• Define, monitor, and enforce governance controls that are measurable, auditable, and aligned with regulatory and risk management expectations Establish governance processes that ensure consistency, scalability, and sustainability across the enterprise
• Data & AI Governance Maturity and Assessment Conduct formal maturity assessments aligned to frameworks such as DCAM and DAMA-DMBOK
• Develop and maintain capability models, maturity scoring methodologies, and assessment criteria across data and AI governance domains
• Perform gap analyses and define actionable remediation roadmaps to enhance governance capabilities
• Establish repeatable processes for evidence collection, validation, and documentation to support assessments and audits
• Produce maturity scorecards and executive-level reporting on governance effectiveness and progress
• Data & AI Lifecycle Governance
• Ensure governance across the full lifecycle of data and AI assets, including data creation, ingestion, transformation, storage, usage, retention, and disposal, as well as model development, validation, deployment, monitoring, and retirement
• Align lifecycle controls with regulatory, compliance, and risk management requirements
• Promote consistent lifecycle management practices and standards across the enterprise.

AI Governance & Enablement

• Support the development and implementation of AI governance practices aligned with enterprise data governance and risk frameworks
• Partner with Data Science, Analytics, and Technology teams to enable responsible, scalable AI and advanced analytics adoption
• Establish governance processes for the AI/ML lifecycle, including model documentation, validation, monitoring, and traceability
• Promote adherence to principles of model transparency, explainability, fairness, and accountability
• Ensure alignment between data quality, lineage, and model performance requirements
• Support auditability and traceability of AI models and their under