Data Analytics Engineer, College of Natural Sciences

University of Texas at Austin

2d•Hybrid

About The Position

The Data Analytics Engineer designs, develops, and supports integrated data solutions that enable informed decision‑making across academic, research, and administrative functions of the university. This role plays a key part in managing and integrating data across multiple environments, including combining local data with central campus datasets within the UT Data Hub framework. Working closely with academic units, central offices, researchers, and IT partners, this position translates institutional needs into robust, efficient, and scalable data structures while operating within university data governance, privacy, and compliance requirements.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field, or equivalent professional experience.
Strong technical expertise in using SQL to query large databases, manipulate and validate data, implement business logic, and analyze data.
Excellent knowledge of databases, data flows, and data manipulation in both operational and analytical contexts.
Proven ability to synthesize complex and/or ambiguous information from multiple sources using a variety of tools and techniques to transform that information into consumable insights.
Ability to communicate with a wide range of stakeholders and collaborate effectively in a higher-education or enterprise setting.
Proven prioritization and organizational skills with the ability to handle multiple projects simultaneously.
Excellent written and verbal communication skills, critical thinking and creative problem-solving, and attention to detail.
Demonstrated ability to maintain a high level of professionalism.
Relevant education and experience may be substituted as appropriate.

Nice To Haves

Experience working in a higher education or research environment.
Familiarity with institutional data domains (e.g., student, enrollment, research, finance, HR).
Experience working with cloud data platforms or analytics services and PostgreSQL environments.
Experience supporting business intelligence or analytics tools (e.g., Power BI, Tableau).
Knowledge of data governance, stewardship, and compliance in higher education.
Experience collaborating across decentralized organizations.
Familiarity with UT Austin's administrative computing environment.
Experience with metadata management software.

Responsibilities

Develops, codes, validates, and implements relational databases and integrated data structures that support analytics.
Collaborates with functional partners to collect business requirements and develop and refine business logic.
Collaborates with technical partners to locate, clean, and orchestrate source data.
Crafts code for the translation and transformation of functional business logic into a high-scale database environment that can readily support the production of descriptive, predictive, and prescriptive analytics and consumable reports, dashboards, and other tools to support University decision-making.
Understands how to best design database structures for meaningful data visualizations and interactive tools.
Engages in best practices in data analytics and data engineering - including robust validation processes, testing/deployment procedures, algorithms for data mining, version control and code integration, thorough documentation, and automation and streamlining of data processing pipelines.
Applies creativity and flexibility to finding solutions for new institutional data challenges.
Assists with special projects and ad hoc data requests as needed.
Works both independently and collaboratively with cross-functional teams to develop exceptional data products to meet university needs.
Participates in data project lifecycle - from conception, requirements gathering and design, documentation, development and testing through deployment.
Engages and communicates with partners and stakeholders to manage requests, expectations, and deliverables.
Effectively communicates project status, progress, risks, and issues to drive project to completion.
Develops an in-depth knowledge of university data processes and technologies.
Handles confidential information with tact and discretion.