Director, Data Governance and Analytics Experience - 991326

Nova Southeastern University (NSU) was founded in 1964 and is a not-for-profit, independent university with a reputation for academic excellence and innovation.

About the role

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.

Responsibilities

• 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.
• Establishes governance as an embedded, domain-based operating capability that directly supports data products, semantic models, analytics experiences, process improvement, and institutional decision-making.
• Builds trusted relationships with academic, administrative, and operational leaders to understand business processes, decisions, pain points, information needs, and opportunities for improvement.
• 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.
• Establishes governance forums, domain councils, stewardship structures, decision rights, escalation paths, and accountability practices.
• 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.
• Defines data owner, data steward, business process owner, data product owner, metric owner, and technical owner responsibilities.
• Owns and matures the institutional business glossary, metric and KPI dictionary, enterprise taxonomy, domain vocabularies, metadata standards, and business-context documentation.
• Leads business metadata management, including asset descriptions, ownership, domain attribution, glossary alignment, certification, classification review, usage context, and discoverability.
• Partners with Information Technology and platform administrators on Microsoft Purview configuration, source registration, technical scanning, lineage capture, classification, and metadata integration.
• Guides the Metadata & Data Governance Analyst in curating Purview assets, connecting business terms to technical assets, monitoring metadata completeness, and improving catalog usability.
• Establishes a data quality framework covering critical data elements, quality dimensions, business rules, thresholds, issue intake, root-cause analysis, remediation, escalation, ownership, and monitoring.
• Facilitates resolution of conflicts involving definitions, metrics, ownership, data quality, access, business rules, and appropriate use.
• Defines governance and experience readiness criteria for domain data products and semantic models.
• 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.
• Ensures each data product and semantic model has an identified audience, business purpose, owner, steward, definitions, quality expectations, metadata, certification status, and adoption plan.
• Establishes analytics experience standards for dashboards, reports, self-service analytics, financial reporting, operational reporting, mobile experiences, accessibility, navigation, visualization, storytelling, and executive communication.
• Leads the design of intuitive, consistent, and role-appropriate analytics experiences using Power BI, Inforiver, Microsoft Fabric, and other approved technologies.
• Guides Analytics Experience Analysts in dashboard and report design, user research, prototyping, requirements validation, usability testing, storytelling, accessibility, performance, and adoption.
• Shifts analytics delivery away from responding to report requests and toward understanding the decisions, behaviors, processes, and outcomes that analytics should improve.
• Establishes a tiered analytics experience model, distinguishing certified institutional reporting, domain analytics, governed self-service, exploratory analysis, and executive decision-support products.
• Leads the rationalization and retirement of reports that are duplicated, unused, inconsistent, unsupported, or disconnected from certified semantic models.
• Develops and maintains feedback mechanisms that capture user satisfaction, trust, ease of use, unmet needs, adoption barriers, and opportunities for product improvement.
• Creates communication, onboarding, training, office-hours, documentation, data literacy, and change-management programs for data owners, stewards, analysts, leaders, and business users.
• Enables business superusers to use certified semantic models and Gold-layer data responsibly through training, templates, standards, support, and clear usage expectations.
• Promotes data-informed process improvement by helping domains connect analytical insights with operational actions, behaviors, accountability, and measurable outcomes.
• Supports responsible AI readiness by improving data meaning, metadata, ownership, quality, transparency, semantic consistency, and appropriate-use guidance.
• Maintains an integrated portfolio of domain needs, governance decisions, analytics experience opportunities, adoption priorities, and organizational change requirements.
• Coaches, mentors, and develops Analytics Experience Analysts, the Metadata & Data Governance Analyst, and the Data Product & Domain Engagement Analyst.
• 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.
• Prepares executive communications, governance recommendations, domain roadmaps, adoption updates, experience assessments, and decision materials.
• Promotes a culture of curiosity, empathy, transparency, accountability, shared ownership, continuous improvement, and responsible data use.
• Completes special projects as assigned.
• Performs other duties as assigned or required.

Requirements

• Knowledge:
  • Expertise in enterprise and domain-based data governance, stewardship, ownership, decision rights, governance adoption, and operating-model design.
  • Strong knowledge of metadata management, business glossaries, taxonomies, data catalogs, lineage, classifications, certification, semantic consistency, and data-asset management.
  • Strong understanding of data quality management, including critical data elements, business rules, monitoring, issue intake, remediation, root-cause analysis, and accountability.
  • Understanding of data products, semantic models, certified metrics, data contracts, dashboards, self-service analytics, reporting standards, and modern analytics delivery.
  • Experience with stakeholder research, requirements discovery, facilitation, product discovery, process mapping, change management, and adoption planning.
  • Understanding of user-centered analytics design, visualization principles, accessibility, storytelling, financial and operational reporting, and decision enablement.
  • Familiarity with Microsoft Purview, Microsoft Fabric, Power BI, Inforiver, or comparable governance, metadata, catalog, and analytics technologies.
  • Understanding of data privacy, security, regulatory expectations, responsible data use, and policy-based access.
  • Knowledge of AI-readiness foundations, including metadata, semantic consistency, transparency, quality, ownership, and responsible-use expectations.
  • Exceptional written, verbal, facilitation, presentation, conflict-resolution, and executive-communication skills.
  • Strong program and project management skills, including roadmap development, prioritization, implementation planning, process design, change management, and value measurement.
• Skills:
  • Domain Partnership: Exceptional ability to build credibility, listen actively, understand business processes, and create trusted relationships with diverse stakeholder groups.
  • Facilitation and Communication: Ability to lead complex conversations, surface disagreement, resolve ambiguity, explain technical concepts clearly, and guide groups toward decisions.
  • Data Governance Leadership: Ability to define governance strategies, stewardship structures, decision rights, standards, workflows, and accountability models.
  • Product and Requirements Discovery: Ability to move stakeholders from report requests to clearly defined problems, users, decisions, outcomes, requirements, and acceptance criteria.
  • Metadata and Knowledge Management: Ability to organize definitions, taxonomies, glossaries, classifications, ownership, lineage context, and asset information for discovery and reuse.
  • Data Quality Management: Ability to define critical data elements, rules, thresholds, monitoring, issue-management, remediation, and ownership practices.
  • Analytics Experience Design: Understanding of user-centered design, dashboard usability, financial and operational reporting, visualization, accessibility, navigation, storytelling, and self-service analytics.
  • Change Management: Ability to drive adoption through communication, education, coaching, engagement, feedback, reinforcement, and measurable outcomes.
  • Data Literacy: Ability to help stakeholders interpret information, understand metrics, ask better questions, recognize limitations, and use data appropriately.
  • Executive Communication: Ability to create concise, compelling, leadership-ready communications that connect governance and analytics investments to institutional outcomes.
• Abilities:
  • Build trust across business and technical groups with different priorities, vocabularies, levels of data fluency, and decision-making authority.
  • Translate institutional needs into actionable governance decisions, product requirements, analytics experiences, and organizational improvements.
  • Recognize when a request reflects a reporting need, data-quality issue, process problem, definition conflict, training need, or decision-rights gap.
  • Facilitate difficult conversations involving ownership, accountability, data quality, conflicting definitions, access, privacy, and institutional priorities.
  • Connect governance activities to visible business value rather than treating governance as a compliance or documentation exercise.
  • Evaluate analytics experiences from the user’s perspective.

Benefits

• Competitive salaries
• Comprehensive benefits package including tuition waiver
• Retirement plan
• Excellent medical and dental plans