Data Governance & Enablement Manager

BankUnited, Inc.•Miami Lakes, FL

About The Position

BankUnited's Technology Data Management team is redefining how the Bank uses data and AI by helping teams align data assets and analytics capabilities across sources and platforms to inform business outcomes. The Data Governance & AI Enablement Manager develops and advances the Bank’s data and AI governance strategy and operating model. The role establishes, operationalizes, and continually matures enterprise data governance capabilities while enabling responsible, scalable AI adoption. It uses industry-standard frameworks such as DCAM and DAMA-DMBOK to assess and improve data management maturity, with a focus on control, transparency, and value realization across the data and AI lifecycle. The position treats data as a strategic enterprise asset and helps ensure AI and analytics initiatives use trusted, well-governed data and meet risk and regulatory expectations. The manager leads governance framework design and implementation, conducts maturity assessments, establishes lifecycle controls, supports AI governance and enablement, and measures governance effectiveness. The role also coordinates data and AI use cases, supports data quality and metadata practices, maintains governance documentation, and collaborates with business, technology, analytics, risk, compliance, and audit stakeholders.

Requirements

Bachelor’s degree from an accredited college or university in information technology, computer science, business administration, or a related field is preferred.
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 such as DCAM or 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.
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 such as AWS, Azure, Snowflake, or Databricks.
Ability to translate governance concepts into quantifiable metrics and maturity models.
Strong analytical, problem-solving, organizational, written communication, and verbal communication skills, including the ability to present to senior leadership.
Ability to work cross-functionally and influence stakeholders without direct authority.
Adhere to applicable laws and regulatory guidance and identify and report suspicious activity.

Nice To Haves

Experience supporting AI/ML initiatives, advanced analytics, or model governance practices.
Familiarity with Agile/DevOps methodologies and tools such as Jira and Confluence.
Working knowledge of SQL and relational databases.
Certified Data Management Professional (CDMP) – DAMA certification.
DCAM (EDM Council or equivalent) certification.
CISA, CRISC, or similar certification.
Familiarity with Model Risk Management frameworks such as SR 11-7.

Responsibilities

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 data ownership, stewardship, and accountability.
Align governance frameworks with industry standards such as DCAM, DAMA-DMBOK, and COBIT; define governance capabilities, control objectives, and measurable outcomes.
Implement and operationalize governance frameworks across business, data, and technology teams; drive stakeholder adoption and adherence to policies and standards.
Support governance committees and data/AI communities, and define, monitor, and enforce measurable, auditable controls aligned with regulatory and risk expectations.
Conduct data and AI governance maturity assessments; develop capability models, scoring methodologies, and assessment criteria; perform gap analyses and create remediation roadmaps.
Establish repeatable evidence collection, validation, and documentation processes; produce maturity scorecards and executive-level reporting.
Ensure governance across data and AI asset lifecycles, including data creation through disposal and model development through retirement, aligned with regulatory, compliance, and risk requirements.
Support AI governance practices and AI/ML lifecycle processes, including model documentation, validation, monitoring, and traceability; promote transparency, explainability, fairness, and accountability.
Partner with Data Science, Analytics, and Technology teams to enable responsible AI and advanced analytics adoption, aligning data quality and lineage with model performance requirements.
Coordinate with lines of business to develop and prioritize data and AI use cases; assess feasibility, risk, and governance readiness; define and measure business value realization.
Define and track governance KPIs and KRIs, maintain maturity tracking, and improve processes based on assessments, metrics, and stakeholder feedback.
Perform data quality assessments, identify issues, support remediation, and support data catalog, metadata, and lineage tool implementation and adoption.
Develop and maintain governance policies, standards, procedures, guidelines, and training; promote consistent use of data catalogs, business glossaries, tools, and processes.
Partner with Risk, Compliance, and Internal Audit to support audit readiness, control validation, audit and regulatory inquiries, and evidence documentation.
Adhere to applicable federal and state laws and regulatory guidance, including financial-services and anti-money-laundering requirements; identify and report suspicious activity.