Data Governance & AI Enablement ManagerSUMMARYBankUnited'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 RESPONSIBILITIESFramework 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 underlying data assets
Data & AI Value Enablement- Coordinate across lines of business to develop and prioritize data and AI use cases aligned to business outcomes
- Partner with stakeholders to define and measure value realization, including revenue enablement, cost optimization, and risk reduction
- Evaluate feasibility, risk, and governance readiness of data and AI initiatives
- Prioritize governance efforts based on business impact and criticality of data and AI assets
- Monitoring, Metrics & Continuous Improvement
- Define and track KPIs and KRIs for governance effectiveness (e.g., data quality, ownership coverage, lineage completeness, model governance adherence)
- Establish maturity tracking mechanisms to measure progress over time
- Continuously improve governance processes based on assessment results, metrics, and stakeholder feedback
Data Management, Quality & Metadata- Perform data quality assessments, identify issues, and support remediation efforts
- Support implementation and adoption of data catalog, metadata, and lineage management tools
- Ensure governance controls are testable, measurable, and aligned to defined standards
- Support evidence-based validation of data quality and governance effectiveness
Documentation, Training & Adoption- Develop and maintain governance documentation, including policies, standards, procedures, and guidelines
- Provide training and education to support adoption of governance practices and tools
- Promote consistent use of data catalogs, business glossaries, and governance processes
- Cross-Functional Collaboration
- Work with Data Analytics, Data Science, and business stakeholders to capture and define data and AI requirements
- Partner with Risk, Compliance, and Internal Audit teams to ensure governance practices support audit readiness and control validation
- Collaborate across IT and business functions to align governance with enterprise priorities
ESSENTIAL DUTIES AND RESPONSIBILITIES- Develop, document, maintain, and enforce data and AI governance policies, standards, and controls
- Define and evaluate governance controls to ensure consistency, effectiveness, and auditability
- Develop and maintain governance KPIs, KRIs, and maturity scorecards
- Conduct maturity assessments and communicate findings and recommendations to leadership
- Produce gap assessments and remediation roadmaps
- Ensure alignment with lifecycle controls and regulatory expectations
- Support audit and regulatory inquiries by providing governance evidence and documentation
- Drive adoption of governance tools and best practices across the enterprise
- Adhere to applicable federal and state laws and regulatory guidance, including those related to financial services and anti-money laundering
- Identify and report suspicious activity
EDUCATIONBachelor's degree from an accredited college or university in information technology, computer science, business administration, or a related field preferred
EXPERIENCE- 7+ years of experience in data governance, data management, or related disciplines
- Experience implementing or operating within data governance frameworks and/or maturity models
- Experience with data governance maturity frameworks (e.g., DCAM, DAMA-DMBOK)
- Experience conducting assessments, audits, or capability evaluations
- Experience defining and implementing governance metrics and performance frameworks
- Experience supporting risk, compliance, or regulatory-driven initiatives
- Experience with data cataloging tools such as Alation, Collibra, Informatica, or similar
- Experience supporting AI/ML initiatives, advanced analytics, or model governance practices is preferred
- Familiarity with Agile/DevOps methodologies and tools such as Jira and Confluence
KNOWLEDGE, SKILLS AND ABILITIES- Strong knowledge of enterprise data management and governance frameworks
- Understanding of data as an asset principles and value measurement approaches
- Familiarity with AI/ML lifecycle concepts, including model development, validation, deployment, and monitoring
- Awareness of AI governance principles, including explainability, fairness, accountability, and transparency
- Ability to design and implement scalable, measurable governance controls
- Knowledge of data lifecycle management, metadata, lineage, and data quality processes
- Familiarity with modern data architectures and platforms (e.g., AWS, Azure, Snowflake, Databricks)
- Ability to translate governance concepts into quantifiable metrics and maturity models
- Strong analytical, problem-solving, and organizational skills
- Strong written and verbal communication skills, including the ability to present to senior leadership
- Ability to work cross-functionally and influence stakeholders without direct authority
- Working knowledge of SQL and relational databases is a plus
PREFERRED CERTIFICATIONS- Certified Data Management Professional (CDMP) - DAMA
- DCAM (EDM Council or equivalent)
- CISA, CRISC, or similar certifications
- Familiarity with Model Risk Management frameworks (e.g., SR 11-7) is a plus
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