Director, Data Products & Analytics Engineering - 992052

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.

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.

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.

Major (if required):

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.

leading modernization initiatives involving cloud-based data and analytics platforms.

Master’s degree preferred. 2.

with Microsoft Fabric, Power BI, OneLake, Purview, Azure, or related Microsoft data services. 3.

establishing enterprise semantic-model standards, data product practices, analytics engineering processes, or governed self-service capabilities. 4.

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

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.