Director, Data Products & Analytics Engineering – 992052

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.

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