Data Analytics Engineer

Job Posting Title:

Data Analytics Engineer

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Hiring Department:

Financial and Administrative Services

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Position Open To:

All Applicants

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Weekly Scheduled Hours:

40

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FLSA Status:

To Be Determined at Offer

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Earliest Start Date:

Ongoing

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Position Duration:

Limited Duration Based on Business Need

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Location:

AUSTIN, TX

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Job Details:

General Notes

This requisition will be used to fill one fixed term position expected to end October 31, 2026.

Reporting to the Data Business Partner (DBP) for the VP/CFO and VP for People and Talent business teams, provide business leaders with direction as a data analytics engineer subject matter expert. Your primary object is to deliver sustainable, accurate, and automated analytics including data modeling, data analytics, and business reporting visualizations.

You’ll collaborate with UT’s Data to Insights (D2I) team, IT functional teams, primary business stakeholders, and downstream data consumers to remove barriers, solve complex data challenges, and ultimately deliver on your primary objective. You will assist and advice your engineering counterparts to build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using industry leading ‘big data’ technologies. You’ll build automated foundational data processes, integrating data quality monitoring and alert systems while integrating large, complex data sets that meet business requirements.  

The University of Texas at Austin provides an outstanding benefits package to staff, including: 

• Competitive health benefits (Employee premiums covered at 100%; family premiums at 50%) 
• Vision, dental, life, and disability insurance options 
• Paid vacation, sick leave, and holidays 
• Teachers Retirement System of Texas (a defined benefit retirement plan) 
• Additional voluntary retirement programs: tax sheltered annuity 403(b) and a deferred compensation program 457(b) 
• Flexible spending account options for medical and childcare expenses 
• Training and conference opportunities 
• Tuition assistance 
• Athletic ticket discounts 
• Access to UT Austin's libraries and museums 
• Free rides on all UT Shuttle and Capital Metro buses with staff ID card 

For more details, please see: https://hr.utexas.edu/prospective/benefits and https://hr.utexas.edu/current/services/my-total-rewards 

This position requires you to maintain internet service and a mobile phone with voice and data plans to be used when required for work.

Must be authorized to work in the United States on a full-time basis for any employer without sponsorship. Must be located in the state of Texas.

Purpose

We are seeking an Analytics Engineer to join the Data Business Partner 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 Visualizations

• Processes, transforms, analyzes, interprets, builds, and disseminates data into meaningful, eye-catching, and consumable data visualizations, reports, dashboards, and other tools to support University decision-making.
• Applies advanced coding, data visualization techniques, and other analytical skills to translate business logic and produce deliverables in response to routine and non-routine data requests using BI tools such as PowerBI or Tableau.
• Develops, codes, validates, and implements relational databases and integrated data structures that support analytics.
• 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.
• Appli