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, a nd 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

EDUCATION Bachelor’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