Analytics Engineer

• 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.

Analytics Engineer – Student Success

• Support the data and analytics team in transforming raw data into meaningful insights
• Assist in building and maintaining data pipelines under guidance from senior team members
• Support data cleaning, transformation, and validation processes
• Help document data sources, transformations, and workflows
• Work with team members to understand basic business requirements
• Assist in developing simple data models
• Support creation and maintenance of dashboards and reports
• Participate in team meetings, code reviews, and knowledge-sharing sessions
• Collaborate with cross-functional teams including IT, HR, Finance, and Operations
• Learn and apply best practices in analytics engineering, data modeling, and data governance.

Analytics Engineer Intern

• Collaborate with stakeholders to understand business requirements and translate them into scalable and efficient data analytics solutions.
• Possess in-depth knowledge and hands-on experience with Microsoft Fabric ecosystem (Lakehouse, PySpark/SQL notebooks, data pipelines, and deployment pipelines).
• Understanding Microsoft Dynamics as a data source a big plus.
• Design data models that bridge the Fabric Lakehouse, Power BI semantic models, and reporting layers, ensuring consistency between raw, curated, and business-facing data.
• Optimize data models to support efficient data storage, retrieval, and analysis, ensuring data integrity, accuracy, and reliability.
• Knowledge of Fabric capacity (CU) optimization a big plus.
• Implement data integration pipelines using Microsoft Fabric and other relevant tools to extract, transform, and load data from various sources into a unified data platform.
• Create visually appealing and interactive dashboards, reports, and visualizations using Microsoft Power BI and DAX language, enabling users to gain valuable insights from data.
• Identify performance bottlenecks and implement optimizations to improve data processing, query performance, and overall system efficiency.
• Collaborate with cross-functional teams, providing technical leadership and guidance to data engineers, analysts, and other stakeholders to ensure successful project delivery.
• Keep abreast of the latest advancements in data analytics technologies (Microsoft based and others) and industry trends, continuously enhancing your expertise and applying best practices in solution design and implementation.

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Analytics Engineer – Student Success

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