Enterprise Data Governance Consultant

Eitacies Inc•Nashville, TN

5h•Hybrid

About The Position

The Enterprise Data Governance Consultant will provide support for AI Governance, Privacy, Risk, and Compliance, aligning data governance activities to business value, mission priorities, compliance needs, and enterprise data strategy. This role will also support the implementation of data governance tools, templates, and documentation practices, and serve as a facilitator for governance activities. Additionally, the consultant will help sustain the adoption of data governance practices through clear communications, change management planning, and program marketing, as well as develop and deliver data governance learning products.

Requirements

Experience supporting data governance, data management, enterprise information management, organizational change management, training, or stakeholder engagement programs.
Experience working with state government, local government, public-sector programs, or highly regulated environments.
Familiarity with DAMA-DMBOK, data stewardship, metadata management, data quality management, data classification, data lifecycle management, and data governance maturity models.
Familiarity with AI governance concepts, responsible AI principles, or frameworks such as the NIST AI Risk Management Framework.
Experience developing and delivering training, workshops, presentations, job aids, communications, and adoption campaigns.
Experience supporting governance councils, committees, working groups, or cross-functional decision-making bodies.
Experience with enterprise data catalog, metadata, business glossary, data inventory, issue tracking, collaboration, or knowledge management tools.
Ability to facilitate cross-functional meetings with business, technology, privacy, security, legal, compliance, and executive stakeholders.
Ability to develop practical data governance strategies, implementation plans, training plans, communications, and adoption materials.
Ability to translate complex data governance, data management, metadata, data quality, privacy, and AI governance concepts into plain language.
Ability to build trust with stakeholders and guide groups toward decisions, documented actions, and measurable progress.
Ability to apply change management practices to support adoption of new roles, responsibilities, processes, and governance behaviors.
Ability to develop business value cases for data governance initiatives.
Ability to support governance documentation aligned to frameworks, policies, procedures, standards, controls, and audit expectations.
Understanding of data governance operating models, data ownership, data stewardship, metadata, data catalogs, data quality, data lifecycle management, data sharing, and data risk management.
Ability to work independently while coordinating with the Enterprise Data Governance Manager, Data Governance Analyst, and stakeholders.
Ability to produce clear, professional, executive-ready written products.

Nice To Haves

Relevant certifications are desirable but not required, such as CDMP, Prosci/Change Management, PMP, CISA, CIPM, or related credentials.

Responsibilities

Incorporate responsible AI, privacy, security, and compliance considerations into data governance practices.
Support awareness of AI governance concepts, including ethical, transparent, explainable, accountable, and human-reviewed use of data and AI-enabled systems.
Document AI-related data use cases, data sources, stewardship responsibilities, risk considerations, and governance checkpoints.
Coordinate with privacy, security, legal, records, and compliance stakeholders on governance activities involving sensitive, confidential, regulated, or high-risk data.
Facilitate the exploitation of AI technologies to automate and/or streamline data governance practices and artifacts.
Reinforce requirements for data classification, access management, data minimization, authorized use, retention, and secure data sharing.
Support documentation of data-related risks, issues, policy exceptions, remediation actions, and audit evidence.
Promote appropriate safeguards for protected or regulated data, including HIPAA, FERPA, CJIS, IRS Pub. 1075, PCI, FTI, PII, and other applicable standards.
Align data governance activities to business value, mission priorities, compliance needs, and enterprise data strategy.
Support data governance planning, including charters, roadmaps, operating models, stakeholder maps, and implementation plans.
Assist in defining data governance scope, ownership, roles, responsibilities, decision rights, and engagement processes.
Identify value-based business needs, data pain points, data quality risks, and opportunities for improved data sharing and reuse.
Support maturity assessments and develop practical work plans to close governance gaps.
Translate technical data governance concepts into plain-language guidance for various audiences.
Promote consistent use of eight core data governance elements: Organization, Policies, Procedures, Standards/Definitions, Architecture, Data Admin/Controls, Metrics/Reporting, and Audits.
Support the use of data governance tools, templates, and documentation practices.
Complete governance artifacts such as RACI charts, business glossaries, data dictionaries, data inventories, data issue logs, policy/procedure mappings, stewardship plans, and governance roadmaps.
Document data domains, data owners, data stewards, business definitions, data flows, data quality rules, risks, controls, and metrics.
Assist with Playbook-aligned implementation activities and evidence gathering for audit readiness.
Coordinate with the Data Governance Analyst on catalog, metadata, data classification, lineage, and related documentation needs.
Support the use of governance metrics and maturity indicators to monitor progress and demonstrate business value.
Maintain organized repositories of governance artifacts, meeting records, training materials, communications, and implementation evidence.
Serve as a facilitator and working-session lead for governance activities.
Plan, facilitate, and document data governance meetings, workshops, steering committee sessions, and stewardship working groups.
Prepare agendas, meeting materials, decision logs, action item trackers, issue logs, and executive-ready summaries.
Facilitate discussions among data owners, data stewards, business leaders, technology teams, privacy, security, legal, and compliance stakeholders.
Support resolution of cross-functional data issues by clarifying decision points, documenting options, identifying risks, and escalating unresolved matters.
Help operationalize governance committees and recurring governance cadences.
Support business value case development for data governance initiatives.
Develop and execute stakeholder engagement and change management plans for data governance adoption.
Create communication products such as briefings, talking points, newsletters, intranet content, FAQs, one-page summaries, presentations, and adoption messaging.
Promote data governance as a business-enabling capability, not only a compliance activity.
Communicate the value of data governance in terms of trusted data, reduced risk, improved service delivery, better decision-making, and increased efficiency.
Support messaging for managing data as an enterprise asset.
Identify adoption barriers and recommend interventions such as targeted communications, leadership briefings, training refreshers, job aids, and facilitation support.
Develop, maintain, and deliver data governance learning products.
Develop training plans, course outlines, instructor-led materials, self-service learning content, quick reference guides, and role-based learning paths.
Deliver training for data owners, data stewards, data custodians, data producers, data consumers, and governance teams.
Support data fluency training aligned to enterprise data governance methodology, tools, and templates.
Create practical exercises using relevant examples such as data ownership, business glossary terms, data quality dimensions, data classification, data sharing, metadata, and governance meeting practices.
Coordinate training feedback, attendance, adoption metrics, and improvement recommendations.
Reinforce data quality expectations, including accuracy, timeliness, accessibility, completeness, and consistency.