Data Engineer Senior Coordinator

University of Florida•Gainesville, FL

1d•$105,000 - $120,000

About The Position

The University of Florida Lastinger Center for Learning seeks a Data Engineer Senior Coordinator to support its mission of improving the quality of teaching, learning, and childcare. The Center develops and scales educational innovations, using research, practice, policy, and educational technologies to improve outcomes including kindergarten readiness, third-grade reading proficiency, and algebra proficiency. It values a broad range of perspectives and aims to create an environment where team members feel respected, valued, and empowered. The position prepares data infrastructure for data science research, contributes to database and data warehouse/lake design across platforms and computing environments, and helps develop secure, flexible data architecture. It supports developers in expanding data analysis and reporting for internal and client use, mentors colleagues in database and warehouse application development, and oversees policies and procedures that protect database integrity and availability. The role coordinates data priorities with research, evaluation, and data teams in alignment with product OKRs. It analyzes data and system requirements, architectures, dictionaries, and schemas; designs, develops, optimizes, and certifies database schemas; and evaluates data credibility, relevance, and meaning. It also collaborates with product and research teams to identify AI opportunities, establishes practices for responsible and reliable AI-assisted work, evaluates AI tools and frameworks, and develops backend structures for data-layer AI tools supporting Center-wide initiatives. This position is soft-funded and time-limited. Applicants must upload a cover letter and resume. The application must be submitted by 11:55 p.m. Eastern Time on the posting end date. A health assessment is not required.

Requirements

Bachelor’s degree and five years of appropriate experience; appropriate college coursework or vocational/technical training may substitute at an equivalent rate for the required experience.
Submit a cover letter and resume with the application.

Nice To Haves

Familiarity with data-intensive technologies and practices.
Knowledge of SAS, R, Python, and SQL, or data visualization software.
Understanding of compliance relating to disclosure laws, regulations, and other requirements.
Ability to communicate clearly in multiple venues and formats with non-technical audiences.
Expertise in large-scale education data and data from application use.
Experience with data infrastructure and data visualization.
Interest in transforming education through the application of data science.
Commitment to the responsible use of data for the benefit of society.
Proactive, precise, and persistent approach.
Organization and planning skills to ensure timely follow-through.

Responsibilities

Prepare data infrastructure for data science research by building data collection and reporting infrastructure.
Contribute to the development and design of databases and data warehouses or lakes across platforms and computing environments.
Contribute to an overall data architecture that supports business information needs in a flexible and secure environment.
Assist developers in streamlining and expanding analysis and reporting for internal use and client reporting.
Mentor others in developing and designing database and warehouse applications across platforms and computing environments.
Develop, implement, and oversee database policies and procedures to ensure the integrity and availability of databases and accompanying software.
Collaborate with research, evaluation, and data teams to create actionable, high-value analysis and reporting priorities for each product, aligned with OKRs.
Analyze data requirements, application and processing architectures, data dictionaries, and database schemas; design, develop, amend, optimize, and certify schema designs to meet system requirements.
Gather, analyze, and normalize information from business processes, functions, and operations to evaluate data credibility and determine relevance and meaning.
Collaborate with product and research teams to identify opportunities for AI to improve data processing, coordination, or reporting efficiency.
Develop standards and best practices for prompt engineering, model output validation, and AI-assisted code and query generation.
Evaluate and select AI tools, models, and frameworks based on data sensitivity, performance requirements, and long-term maintainability.
Develop backend structures to support AI tools at the data layer for Center-wide initiatives.
Perform other duties as assigned.