Director, Data Products & Analytics Engineering - 992052

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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.

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Primary Purpose:

Leads the design, development, and lifecycle management of the University’s reusable data products, Gold-layer analytical data, and institutional semantic models. Establishes a product-oriented analytics engineering capability that transforms governed institutional data into trusted, reusable, and scalable analytical foundations. Ensures that common dimensions, measures, hierarchies, business logic, and security models are built once and reused across reports, dashboards, self-service analytics, advanced analytics, and AI-enabled applications. Partners closely with Data Governance & Analytics Experience, Information Technology, Data Engineering, enterprise architecture, institutional leaders, and domain stakeholders to translate business needs and governance requirements into technically sound data products and semantic models. Accountable for creating a plug-and-play analytics foundation in Microsoft Fabric and Power BI that allows authorized analysts, report developers, and business superusers to create analytical experiences from governed and certified data without recreating institutional logic

 

Job Category: Exempt

Hiring Range:  Commensurate with experience

Pay Basis:  Annually

Subject to Grant Funding? No  

Essential Job Functions: 

1. Defines and executes the roadmap for institutional semantic models, domain data products, analytics engineering, and Gold-layer analytical data in alignment with the enterprise data and analytics strategy.
2. Establishes a product-oriented analytics engineering operating model organized around reusable domain data products, institutional measures, conformed dimensions, shared business logic, and measurable business outcomes.
3. Leads the design, development, testing, deployment, documentation, and lifecycle management of governed semantic models and analytical data products.
4. Establishes semantic-layer architecture standards, including dimensional models, facts, dimensions, measures, calculation groups, hierarchies, relationships, row-level security, object-level security, naming conventions, and performance expectations.
5. Partners with Information Technology and Data Engineering to define Gold-layer requirements and ensure that Bronze- and Silver-layer data is transformed into reliable, consumption-ready institutional data products.
6. Defines and implements data product lifecycle practices covering discovery, prioritization, design, development, testing, certification, release, monitoring, versioning, enhancement, and retirement.
7. Establishes and maintains reusable enterprise and domain data products for areas such as enrollment, academics, student success, finance, workforce, research, advancement, and institutional operations.
8. Leads the rationalization and consolidation of semantic models, measures, dimensions, facts, reports, and duplicated business logic.
9. Increases the ratio of reports and analytical experiences supported by each certified semantic model, reducing one-report-to-one-model development patterns.
10. Establishes technical standards for data contracts, including schemas, expected fields, grain, refresh expectations, quality requirements, ownership, dependencies, and change-management protocols.
11. Partners with the Director of Data Governance & Analytics Experience to ensure that business definitions, metric standards, stewardship decisions, metadata, quality rules, and certification requirements are incorporated into each data product.
12. Translates approved business definitions and governance decisions into consistent technical calculations, measures, transformations, relationships, and semantic structures.
13. Implements automated testing for data products and semantic models, including reconciliation, completeness, validity, referential integrity, calculation accuracy, refresh reliability, and regression testing.
14. Establishes development, testing, deployment, source-control, and release-management practices for Microsoft Fabric and Power BI asset