Data Governance & Enablement Manager

Job DescriptionData 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 & StrategyLead the design and evolution of the Data & AI Governance framework to support business value realization and regulatory alignmentCollaborate with business and technology stakeholders to establish strong data ownership, stewardship, and accountabilityAlign 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 & OperationsImplement and operationalize governance frameworks across business, data, and technology teamsDrive stakeholder engagement, adoption, and adherence to governance policies and standardsSupport governance committees and data/AI communities as key forums for alignment and decision-makingDefine, 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 enterpriseData & AI Governance Maturity and Assessment Conduct formal maturity assessments aligned to frameworks such as DCAM and DAMA-DMBOKDevelop and maintain capability models, maturity scoring methodologies, and assessment criteria across data and AI governance domainsPerform gap analyses and define actionable remediation roadmaps to enhance governance capabilitiesEstablish repeatable processes for evidence collection, validation, and documentation to support assessments and auditsProduce maturity scorecards and executive-level reporting on governance effectiveness and progressData & AI Lifecycle GovernanceEnsure 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 retirementAlign lifecycle controls with regulatory, compliance, and risk management requirementsPromote consistent lifecycle management practices and standards across the enterprise.AI Governance & EnablementSupport the development and implementation of AI governance practices aligned with enterprise data governance and risk frameworksPartner with Data Science, Analytics, and Technology teams to enable responsible, scalable AI and advanced analytics adoptionEstablish governance processes for the AI/ML lifecycle, including model documentation, validation, monitoring, and traceabilityPromote adherence to principles of model transparency, explainability, fairness, and accountabilityEnsure alignment between data quality, lineage, and model performance requirementsSupport auditability and traceability of AI models and their underlying data assetsData & AI Value EnablementCoordinate across lines of business to develop and prioritize data and AI use cases aligned to business outcomesPartner with stakeholders to define and measure value realization, including revenue enablement, cost optimization, and risk reductionEvaluate feasibility, risk, and governance readiness of data and AI initiativesPrioritize governance efforts based on business impact and criticality of data and AI assetsMonitoring, Metrics & Continuous ImprovementDefine 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 timeContinuously improve governance processes based on assessment results, metrics, and stakeholder feedbackData Management, Quality & MetadataPerform data quality assessments, identify issues, and support remediation effortsSupport implementation and adoption of data catalog, metadata, and lineage management toolsEnsure governance controls are testable, measurable, and aligned to defined standardsSupport evidence-based validation of data quality and governance effectivenessDocumentation, Training & AdoptionDevelop and maintain governance documentation, including policies, standards, procedures, and guidelinesProvide training and education to support adoption of governance practices and toolsPromote consistent use of data catalogs, business glossaries, and governance processesCross-Functional CollaborationWork with Data Analytics, Data Science, and business stakeholders to capture and define data and AI requirementsPartner with Risk, Compliance, and Internal Audit teams to ensure governance practices support audit readiness and control validationCollaborate across IT and business functions to align governance with enterprise prioritiesESSENTIAL DUTIES AND RESPONSIBILITIESDevelop, document, maintain, and enforce data and AI governance policies, standards, and controlsDefine and evaluate governance controls to ensure consistency, effectiveness, and auditabilityDevelop and maintain governance KPIs, KRIs, and maturity scorecardsConduct maturity assessments and communicate findings and recommendations to leadershipProduce gap assessments and remediation roadmapsEnsure alignment with lifecycle controls and regulatory expectationsSupport audit and regulatory inquiries by providing governance evidence and documentationDrive adoption of governance tools and best practices across the enterpriseAdhere to applicable federal and state laws and regulatory guidance, including those related to financial services and anti-money launderingIdentify and report suspicious activityEDUCATIONBachelor’s degree from an accredited college or university in information technology, computer science, business administration, or a related field preferredEXPERIENCE7+ years of experience in data governance, data management, or related disciplinesExperience implementing or operating within data governance frameworks and/or maturity modelsExperience with data governance maturity frameworks (e.g., DCAM, DAMA-DMBOK)Experience conducting assessments, audits, or capability evaluationsExperience defining and implementing governance metrics and performance frameworksExperience supporting risk, compliance, or regulatory-driven initiativesExperience with data cataloging tools such as Alation, Collibra, Informatica, or similarExperience supporting AI/ML initiatives, advanced analytics, or model governance practices is preferredFamiliarity with Agile/DevOps methodologies and tools such as Jira and ConfluenceKNOWLEDGE, SKILLS AND ABILITIESStrong knowledge of enterprise data management and governance frameworksUnderstanding of data as an asset principles and value measurement approachesFamiliarity with AI/ML lifecycle concepts, including model development, validation, deployment, and monitoringAwareness of AI governance principles, including explainability, fairness, accountability, and transparencyAbility to design and implement scalable, measurable governance controlsKnowledge of data lifecycle management, metadata, lineage, and data quality processesFamiliarity with modern data architectures and platforms (e.g., AWS, Azure, Snowflake, Databricks)Ability to translate governance concepts into quantifiable metrics and maturity modelsStrong analytical, problem-solving, and organizational skillsStrong written and verbal communication skills, including the ability to present to senior leadershipAbility to work cross-functionally and influence stakeholders without direct authorityWorking knowledge of SQL and relational databases is a plusPREFERRED CERTIFICATIONSCertified Data Management Professional (CDMP) – DAMADCAM (EDM Council or equivalent)CISA, CRISC, or similar certificationsFamiliarity with Model Risk Management frameworks (e.g., SR 11-7) is a plusJob DetailsJob Type: Full Time - PermanentCategory: Information Technology