Analytics Engineer - Student Success

About the role

Student Success Analytics Engineers play a central role in advancing the university's analytics capabilities in support of student achievement, learning outcomes, and institutional effectiveness. This position works closely with the Institutional Data Engineering team and the Deputy Chief Data & Analytics Officer to develop and maintain the analytical data models that power predictive analytics, student success reporting, and institutional decision-making.

Responsibilities

• Analytic 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.

Qualifications

• Minimum Qualifications: 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.
• Preferred Qualifications: Master's degree in related field, Experience in higher education, Experience with modern data transformation frameworks (e.g., dbt or similar tools), Experience working in cloud-based data platforms or data lake environments, Familiarity with medallion architecture or layered data modeling approaches, Experience supporting predictive analytics or machine learning pipelines, Experience working with higher education data (student information systems, learning management systems, advising platforms), Knowledge of student success analytics, institutional research, or learning analytics.