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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 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 assets.
15. Leads performance optimization for semantic models, Direct Lake solutions, Power BI reports, Fabric workloads, and analytical queries.
16. Defines service expectations for certified data products, including refresh frequency, availability, performance, support, issue response, and change notification.
17. Enables governed self-service analytics by publishing certified semantic models, reusable measures, templates, documentation, sample reports, and development guidance.
18. Partners with Analytics Experience Analysts and business superusers to ensure that semantic models are understandable, discoverable, usable, and aligned with real reporting needs.
19. Evaluates requests for new reports or models to determine whether an existing data product can be reused, extended, or consolidated before creating new assets.
20. Maintains a prioritized data product and semantic model portfolio based on institutional value, domain readiness, reuse potential, regulatory requirements, risk, and team capacity.
21. Supports the use of trusted semantic models and data products by advanced analytics, data science, automation, and AI-enabled applications.
22. Applies responsible AI and automation to analytics engineering activities such as documentation, code generation, testing, reconciliation, quality review, metadata creation, and performance analysis while maintaining human oversight.
23. Coaches, mentors, and develops Analytics Engineers and Semantic Modelers in dimensional modeling, Microsoft Fabric, Power BI, data products, semantic design, testing, documentation, and product-oriented delivery.
24. Establishes measures of analytics engineering performance and value, including semantic model reuse, report-to-model ratio, delivery time, duplicate-asset reduction, refresh reliability, performance, defect rates, adoption, and stakeholder value.
25. Prepares technical roadmaps, architecture recommendations, investment proposals, progress reports, and executive-level updates.
26. Promotes continuous improvement, experimentation, automation, reusable patterns, and engineering discipline across the data and analytics function.
27. Completes special projects as assigned.
28. Performs other duties as assigned or required.
Job Requirements:
Required Knowledge, Skills, & Abilities:
Knowledge:
1. Advanced knowledge of analytics engineering, semantic modeling, dimensional modeling, data warehousing, lakehouse architecture, and reusable analytical data products.
2. Strong knowledge of Microsoft Fabric, Power BI, OneLake, Fabric Lakehouse, Fabric Warehouse, Direct Lake, semantic models, or comparable modern analytics platforms.
3. Demonstrated ability to design and manage enterprise or domain semantic models supporting multiple reports, use cases, and audiences.
4. Knowledge of data product management, product lifecycle practices, data contracts, service expectations, ownership, adoption, and value measurement.
5. Understanding of data governance, stewardship, metadata, certification, data quality, privacy, security, and controlled self-service analytics.
6. Experience implementing testing, reconciliation, source control, release management, deployment automation, monitoring, and technical documentation.
7. Strong leadership, prioritization, planning, coaching, stakeholder-management, and technical decision-making skills.
8. Effective written and verbal communication skills with the ability to produce clear architecture decisions, technical standards, roadmaps, product documentation, and executive materials.
Skills:
1. Semantic Architecture: Ability to design scalable semantic layers, dimensional models, shared measures, hierarchies, calculation patterns, and security structures.
2. Data Product Management: Ability to manage analytical assets as products with defined users, outcomes, owners, roadmaps, quality expectations, service levels, adoption measures, and lifecycles.
3. Analytics Engineering: Strong ability to transform curated data into reliable, tested, documented, and reusable analytical structures.
4. Microsoft Fabric and Power BI: Ability to apply Fabric, OneLake, lakehouse, warehouse, Direct Lake, semantic model, and Power BI capabilities to enterprise analytical solutions.
5. Data Modeling: Advanced knowledge of dimensional modeling, star schemas, facts, dimensions, grain, slowly changing dimensions, conformed dimensions, and analytical design patterns.
6. Engineering Quality: Ability to establish automated testing, reconciliation, deployment, source-control, documentation, monitoring, and performance-management practices.
7. Architecture Collaboration: Ability to work across Data Engineering, Information Technology, security, governance, and business teams to deliver integrated solutions.
8. Product Prioritization: Ability to prioritize investments based on institutional value, reuse potential, risk, domain readiness, dependencies, and capacity.
9. Team Development: Ability to coach technical professionals, establish standards, and develop a culture of quality, accountability, curiosity, and continuous learning.
10. Technical Communication: Ability to explain semantic models, data products, technical dependencies, and architecture decisions to technical and nontechnical audiences.
Abilities:
1. Translate institutional business needs into reusable analytical capabilities rather than one-time technical solutions.
2. Balance near-term delivery needs with long-term architecture, maintainability, and reuse.
3. Identify opportunities to consolidate duplicated models, reports, transformations, and calculations.
4. Evaluate complex data structures, dependencies, performance issues, security requirements, and technology tradeoffs.
5. Design analytical foundations that support both centrally developed analytics and governed self-service.
6. Partner effectively with governance and domain stakeholders to convert business decisions into implementable technical requirements.
7. Establish repeatable engineering processes that increase delivery speed without sacrificing quality, security, or trust.
Required Certifications/Licensures:
Required Education: Bachelor's Degree
Major (if required):
Required Experience:
1. Seven (7) or more years of progressively responsible experience in analytics engineering, business intelligence, data modeling, data warehousing, semantic modeling, or data product delivery.
2. Three (3) or more years of leadership, supervisory, technical-lead, or team-development experience.
3. Demonstrated experience delivering reusable semantic models or analytical data products supporting multiple business use cases.
4. Experience leading modernization initiatives involving cloud-based data and analytics platforms.
Preferred Qualifications:
1. Master's degree preferred.
2. Experience with Microsoft Fabric, Power BI, OneLake, Purview, Azure, or related Microsoft data services.
3. Experience establishing enterprise semantic-model standards, data product practices, analytics engineering processes, or governed self-service capabilities.
4. Experience working with higher education domains such as enrollment, academics, student success, finance, workforce, research, and institutional operations.
5. Relevant Microsoft, DAMA, data architecture, data engineering, analytics, or product-management certifications preferred.
6. Ellucian Banner experience is a plus.
Is this a safety sensitive position? No
Background Screening Required? Yes
Pre-Employment Conditions:
Must reside in state of Florida.
Sensitivity Disclaimer: Nova Southeastern University is in full compliance with the Americans with Disabilities Act (ADA) and does not discriminate with regard to applicants or employees with disabilities and will make reasonable accommodation when necessary.
NSU is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion, creed, gender, national origin, age, disability, marital or veteran status or any other legally protected status.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 10201319
- Position Id: 4eca80c8d152e320f9d1bd0db062bfd7
- Posted 30+ days ago
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