Analytics Engineer

Salary

Salary Range: $90,000 + depending on qualifications

The Data to Insights (D2I) Initiative at UT Austin is an investment to build a trusted, integrated, and scalable information infrastructure that transforms complex UT data into valued insights for data-informed decisions.

As a team member in the Data to Insights (D2I) Initiative, you will work with a cross-campus team using the latest cloud technologies to build a next-generation data ecosystem that will improve decision-making and advance the university’s mission. We believe the best ideas arise from collaborative work among colleagues from varied backgrounds and experiences. We actively seek diversity of viewpoint and perception in our student, faculty, and staff recruiting and retention practices. If you’re the type of person who loves to learn and wants to know your work has meaning, you may find your career home at UT Austin. Please note that this position is currently funded through August 31, 2026.

Purpose

We are seeking an Analytics Engineer to join the D2I Analytics team. As an Analytics Engineer, you operate at the intersection of data analytics, data engineering, and business intelligence to build robust, efficient, and scalable data structures to support high-quality, nuanced, and timely data analytics to empower strategic decisions and policies by University leadership and stakeholders.

Responsibilities

Data Analytics & Data Engineering:

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.

Analytical Architecture. In collaboration with reporting manager:

Assists with diagramming analytics architecture strategy and roadmap, as well as translating strategy into actionable tasks.
Coordinates the development, promotion, and implementation of select best practices in analytics architecture and data engineering – examples include coding conventions, naming standards for analytical structures, performance tuning, review checklists.
Engages in exploration, evaluation, and implementation of new/improved tools, features, and/or processes, to scale up analytics architecture – including possible transition to an ETL tool.

Collaboration, Communication, & Institutional Knowledge:

Works both independently and collaboratively with cross-functional teams to develop exceptional data products to meet university needs.
Participates in the 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.
Consult with data analysts on specific data and coding questions within and outside of the team.
Handles confidential information with tact and discretion.

Required Qualifications

Bachelor’s degree with quantitative experience or at least 4 years of professional experience in information technology or related field.
Demonstrated passion for using data to answer questions and solve problems.
Demonstrated proficiency 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 effectively with a wide range of stakeholders to gather data requirements and produce appropriate analysis.
Excellent interpersonal skills to positively contribute to collaborative team, influence without authority, and thoughtfully and productively give and receive feedback.
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, documentation habits, and attention to detail.
Demonstrated ability to maintain a high level of professionalism.

Relevant education and experience may be substituted as appropriate.

Preferred Qualifications

Experience working in Amazon Redshift and PostgreSQL environments.
Expert in SQL database design for the purposes of scaling and supporting business intelligence and analytical needs.
Experience with developing and scheduling ETL workflows, preferably with Informatica.
Experience working with Enterprise Data Models.
Experience using R/Quarto for data analysis.
Experience in higher education administration, programming, or analysis. Interest in higher education data, processes, and progress.
Broad and deep knowledge of UT software and data systems.
Familiarity with UT Austin's administrative computing environment.
Knowledge of federal education law and policies related to education records.
Experience with metadata management software.
Experience using Agile methodologies.

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