This position plays a critical role within the Digital Learning Data Science Technologies (DST) team by serving as the technical leader of data engineering in the Digital Learning (DL) division. This role will lead establishment, operation, and evaluation of a data infrastructure that supports DL's strategic decision making and reporting. Overseeing secure collection, cleaning, standardization, transformation and storing of data from various sources, this role should understand higher education’s complex reporting requirements of marketing, admissions, enrollment, academic program, and other student data. This position will work to integrate and model data from internal systems such as Slate, Banner, and Canvas and external sources such as Google Analytics, Salesforce, CPD platforms, and others to help fulfill our team’s mission.

Data Engineering & Infrastructure

• Provide technical leadership for the architecture of the DL’s data & business intelligence infrastructure.
• Gather, store, and model data from internal and external sources to support the DL’s continuous improvement efforts.
• Lead efforts to develop, monitor, and maintain DL’s data warehouse and client/server environment of all relevant data, ensuring data quality. Collaborate closely with the Solutions Engineering (SE) team during the initial stages of the data fabric construction. 
• Develop and manage partnerships with BI experts and data owners throughout the UT System. 
• Collaborate with the DST team to deliver data for division’s reports, dashboards and presentations.
• Provide day-to-day management of BI applications, projects, services and oversee completion and follow through of projects and service requests.
• Prepare and maintain technical documentation describing DL’s data warehouse and client/server environment.

Business Intelligence Leadership & Team Leadership

• Serve as a member of the DL’s Technology leadership team.
• Participate in data governance planning and operation.
• Serve as the subject matter expert of DL’s data engineering and warehousing efforts.
• Evaluate the impact of data engineering innovations and applications intended to improve DL outcomes. Implement innovative or updated applications as needed. 
• Utilize effective project planning techniques to break down projects into tasks and ensure deadlines are met.
• Lead the Data Analyst within the DST team to create computational data models that describe the DL’s data systems. 
• Collaborate with the Data Analyst within the DST team in order to improve the effectiveness of computational models.