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Nova Southeastern University (NSU) was founded in 1964, and is a not-for-profit, independent university with a reputation for academic excellence and innovation. Nova Southeastern University offers competitive salaries, a comprehensive benefits package including tuition waiver, retirement plan, excellent medical and dental plans and much more. NSU cares about the health and welfare of its students, faculty, staff, and campus visitors and is a tobacco-free university.

We appreciate your support in making NSU the preeminent place to live, work, study and grow. Thank you for your interest in a career with Nova Southeastern University.

Primary Purpose:

Leads the University’s domain-based data governance, metadata management, data quality, stewardship, analytics experience, and data adoption capabilities. Serves as the human-centered bridge between institutional business areas and the technical data and analytics organization. Builds trust and shared understanding with academic, administrative, and operational domains; facilitates agreement on definitions, ownership, measures, quality, and priorities; and ensures those decisions are translated into trusted data products and effective analytical experiences. Partners closely with Data Products & Analytics Engineering, Information Technology, Data Engineering, security, institutional leadership, data owners, data stewards, and business users to build an analytics environment that people understand, trust, adopt, and use effectively.

Job Category:Exempt

Hiring Range: Commensurate with experience

Pay Basis: Annually

Subject to Grant Funding?No

Essential Job Functions:

1. Defines and executes the institutional data governance, metadata management, analytics experience, adoption, and data literacy strategy in alignment with the enterprise data and analytics roadmap.
2. Establishes governance as an embedded, domain-based operating capability that directly supports data products, semantic models, analytics experiences, process improvement, and institutional decision-making.
3. Builds trusted relationships with academic, administrative, and operational leaders to understand business processes, decisions, pain points, information needs, and opportunities for improvement.
4. Leads structured product and requirements discovery with domain stakeholders, converting business needs into clear problem statements, desired outcomes, use cases, definitions, acceptance criteria, and prioritized product requirements.
5. Establishes governance forums, domain councils, stewardship structures, decision rights, escalation paths, and accountability practices.
6. Leads the formation and facilitation of the Data Stewardship Council and domain working groups addressing definitions, ownership, metadata, quality, access, policy, analytics, and data product readiness.
7. Defines data owner, data steward, business process owner, data product owner, metric owner, and technical owner responsibilities.
8. Owns and matures the institutional business glossary, metric and KPI dictionary, enterprise taxonomy, domain vocabularies, metadata standards, and business-context documentation.
9. Leads business metadata management, including asset descriptions, ownership, domain attribution, glossary alignment, certification, classification review, usage context, and discoverability.
10. Partners with Information Technology and platform administrators on Microsoft Purview configuration, source registration, technical scanning, lineage capture, classification, and metadata integration.
11. Guides the Metadata & Data Governance Analyst in curating Purview assets, connecting business terms to technical assets, monitoring metadata completeness, and improving catalog usability.
12. Establishes a data quality framework covering critical data elements, quality dimensions, business rules, thresholds, issue intake, root-cause analysis, remediation, escalation, ownership, and monitoring.
13. Facilitates resolution of conflicts involving definitions, metrics, ownership, data quality, access, business rules, and appropriate use.
14. Defines governance and experience readiness criteria for domain data products and semantic models.
15. Partners with the Director of Semantic Data Products & Analytics Engineering to develop data contracts that document business meaning, source expectations, grain, required attributes, quality rules, refresh expectations, ownership, dependencies, and change protocols.
16. Ensures each data product and semantic model has an identified audience, business purpose, owner, steward, definitions, quality expectations, metadata, certification status, and adoption plan.
17. Establishes analytics experience standards for dashboards, reports, self-service analytics, financial reporting, operational reporting, mobile experiences, accessibility, navigation, visualization, storytelling, and executive communication.
18. Leads the design of intuitive, consistent, and role-appropriate analytics experiences using Power BI, Inforiver, Microsoft Fabric, and other approved technologies.
19. Guides Analytics Experience Analysts in dashboard and report design, user research, prototyping, requirements validation, usability testing, storytelling, accessibility, performance, and adoption.
20. Shifts analytics delivery away from responding to report requests and toward understanding the decisions, behaviors, processes, and outcomes that analytics should improve.
21. Establishes a tiered analytics experience model, distinguishing certified institutional reporting, domain analytics, governed self-service, exploratory analysis, and executive decision-support products.
22. Leads the rationalization and retirement of reports that are duplicated, unused, inconsistent, unsupported, or disconnected from certified semantic models.
23. Develops and maintains feedback mechanisms that capture user satisfaction, trust, ease of use, unmet needs, adoption barriers, and opportunities for product improvement.
24. Creates communication, onboarding, training, office-hours, documentation, data literacy, and change-management programs for data owners, stewards, analysts, leaders, and business users.
25. Enables business superusers to use certified semantic models and Gold-layer data responsibly through training, templates, standards, support, and clear usage expectations.
26. Promotes data-informed process improvement by helping domains connect analytical insights with operational actions, behaviors, accountability, and measurable outcomes.
27. Supports responsible AI readiness by improving data meaning, metadata, ownership, quality, transparency, semantic consistency, and appropriate-use guidance.
28. Maintains an integrated portfolio of domain needs, governance decisions, analytics experience opportunities, adoption priorities, and organizational change requirements.
29. Coaches, mentors, and develops Analytics Experience Analysts, the Metadata & Data Governance Analyst, and the Data Product & Domain Engagement Analyst.
30. Establishes measures of governance and analytics experience value, including stakeholder trust, user adoption, satisfaction, metadata completeness, glossary coverage, certified asset usage, report consolidation, data quality resolution, self-service enablement, decision-cycle improvement, and reduction in manual reporting effort.
31. Prepares executive communications, governance recommendations, domain roadmaps, adoption updates, experience assessments, and decision materials.
32. Promotes a culture of curiosity, empathy, transparency, accountability, shared ownership, continuous improvement, and responsible data use.
33. Completes special projects as assigned.
34. Performs other duties as assigned or required.

Job Requirements:

Required Knowledge, Skills, & Abilities:

Knowledge:
1. Expertise in enterprise and domain-based data governance, stewardship, ownership, decision rights, governance adoption, and operating-model design.
2. Strong knowledge of metadata management, business glossaries, taxonomies, data catalogs, lineage, classifications, certification, semantic consistency, and data-asset management.
3. Strong understanding of data quality management, including critical data elements, business rules, monitoring, issue intake, remediation, root-cause analysis, and accountability.
4. Understanding of data products, semantic models, certified metrics, data contracts, dashboards, self-service analytics, reporting standards, and modern analytics delivery.
5. Experience with stakeholder research, requirements discovery, facilitation, product discovery, process mapping, change management, and adoption planning.
6. Understanding of user-centered analytics design, visualization principles, accessibility, storytelling, financial and operational reporting, and decision enablement.
7. Familiarity with Microsoft Purview, Microsoft Fabric, Power BI, Inforiver, or comparable governance, metadata, catalog, and analytics technologies.
8. Understanding of data privacy, security, regulatory expectations, responsible data use, and policy-based access.
9. Knowledge of AI-readiness foundations, including metadata, semantic consistency, transparency, quality, ownership, and responsible-use expectations.
10. Exceptional written, verbal, facilitation, presentation, conflict-resolution, and executive-communication skills.
11. Strong program and project management skills, including roadmap development, prioritization, implementation planning, process design, change management, and value measurement.
Skills:
1. Domain Partnership: Exceptional ability to build credibility, listen actively, understand business processes, and create trusted relationships with diverse stakeholder groups.
2. Facilitation and Communication: Ability to lead complex conversations, surface disagreement, resolve ambiguity, explain technical concepts clearly, and guide groups toward decisions.
3. Data Governance Leadership: Ability to define governance strategies, stewardship structures, decision rights, standards, workflows, and accountability models.
4. Product and Requirements Discovery: Ability to move stakeholders from report requests to clearly defined problems, users, decisions, outcomes, requirements, and acceptance criteria.
5. Metadata and Knowledge Management: Ability to organize definitions, taxonomies, glossaries, classifications, ownership, lineage context, and asset information for discovery and reuse.
6. Data Quality Management: Ability to define critical data elements, rules, thresholds, monitoring, issue-management, remediation, and ownership practices.
7. Analytics Experience Design: Understanding of user-centered design, dashboard usability, financial and operational reporting, visualization, accessibility, navigation, storytelling, and self-service analytics.
8. Change Management: Ability to drive adoption through communication, education, coaching, engagement, feedback, reinforcement, and measurable outcomes.
9. Data Literacy: Ability to help stakeholders interpret information, understand metrics, ask better questions, recognize limitations, and use data appropriately.
10. Executive Communication: Ability to create concise, compelling, leadership-ready communications that connect governance and analytics investments to institutional outcomes.
Abilities:
1. Build trust across business and technical groups with different priorities, vocabularies, levels of data fluency, and decision-making authority.
2. Translate institutional needs into actionable governance decisions, product requirements, analytics experiences, and organizational improvements.
3. Recognize when a request reflects a reporting need, data-quality issue, process problem, definition conflict, training need, or decision-rights gap.
4. Facilitate difficult conversations involving ownership, accountability, data quality, conflicting definitions, access, privacy, and institutional priorities.
5. Connect governance activities to visible business value rather than treating governance as a compliance or documentation exercise.
6. Evaluate analytics experiences from the user’s perspective and identify barriers to comprehension, trust, accessibility, adoption, and action.
7. Structure complex definitions, requirements, metadata, policies, workflows, and stakeholder decisions into repeatable practices.
8. Influence without authority and maintain momentum across a highly matrixed organization.

Required Certifications/Licensures:

Required Education:Bachelor's Degree

Major (if required):Information Systems, Data/Information Management, Analytics, Business Analytics, Information or Library Sciences, Computer Science, Data Science, Business Administration, or related field.

Required Experience:

1. Ten (10) or more years of progressively responsible experience in data governance, analytics, data management, business intelligence, metadata management, data quality, product management, business analysis, or organizational transformation.
2. Five (5) or more years of leadership, supervisory, program-leadership, or team-development experience.
3. Demonstrated experience building partnerships across business and technical teams and facilitating decisions involving definitions, ownership, quality, requirements, or analytics.
4. Experience implementing data governance, metadata, data quality, analytics adoption, or user-experience capabilities in a complex organization.

Preferred Qualifications:

1. Master’s Degree
2. Experience with Microsoft Purview, Microsoft Fabric, Power BI, Inforiver, Azure, or related technologies.
3. Experience establishing domain governance, stewardship councils, business glossaries, metric dictionaries, metadata standards, catalog-curation processes, or data-quality programs.
4. Experience leading analytics requirements discovery, dashboard rationalization, self-service enablement, user research, usability assessment, or data literacy programs.
5. Experience working in higher education or another complex, decentralized, highly matrixed environment.
6. Relevant certifications in data governance, change management, business analysis, product management, user experience, Microsoft analy