Analytics Engineer – Student Success

Responsibilities

• Analytics Data Modeling Design and maintain analytics-ready data models that support student achievement, learning outcomes assessment, and student success initiatives.
• Develop curated data layers within the university’s medallion data architecture to support reporting, dashboards, and predictive analytics (silver and gold layers).
• Transform complex institutional data sources into structured analytical datasets suitable for downstream analytics tools.
• Ensure data transformations are scalable, well-documented, and maintainable.
• Optimize data transformations to support high-performance dashboards and analytics applications.
• Implement dimensional models and semantic layers to support consistent institutional metrics.
• Data Governance and Documentation Document analytical datasets, transformations, and metric definitions.
• Contribute to institutional data governance processes by helping define and standardize key student success metrics.
• Ensure analytics data models align with institutional definitions and reporting standards.
• Support data quality monitoring and validation processes.
• Collaboration and Institutional Impact Partner with stakeholders to understand analytics needs.
• Translate institutional questions about student learning and success into scalable analytics solutions.
• Contribute to the development of a modern analytics environment supporting data-informed decision-making across the university.
• Support strategic analytics initiatives led by the Office of Data & Analytics.

• Bachelor’s degree in data science, computer science, information systems, statistics, or a related field plus one year of related experience.
• Experience working with relational databases and SQL.
• Experience developing data transformations or data pipelines.
• Familiarity with data modeling concepts (e.g., dimensional modeling, star schemas).
• Experience working with analytics or reporting tools such as Power BI, Tableau, or similar platforms.
• Strong problem-solving and analytical skills.
• Ability to communicate technical concepts to non-technical stakeholders.

Demonstrates expertise in analytics data modeling, including the design and maintenance of analytics-ready data models and data transformations. Proficient in collaborating with stakeholders to develop scalable analytics solutions that support student success initiatives and data-informed decision-making.