UTAustin

Senior Data Engineer

Texas Full time

Job Posting Title:

Senior Data Engineer

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Hiring Department:

Enterprise Technology - Data to Insights (D2I)

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Position Open To:

All Applicants

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Weekly Scheduled Hours:

40

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FLSA Status:

Exempt

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Earliest Start Date:

Immediately

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Position Duration:

Expected to Continue Until Dec 19, 2026

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Location:

Texas

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Job Details:

General Notes

This is a fixed term position that is expected to continue for a 1-year limited term from start date with a possibility for extension.

Flexible work arrangements are available for this position, including the ability to work 100% remotely. Remote work for individuals who reside outside Texas but within the United States and its territories will be considered and requires Central Office approval.

This position provides life/work balance with typically a 40-hour work week and travel limited to training (e.g., conferences/courses).

Enterprise Technology is dedicated to supporting the mission of the University of Texas at Austin of unlocking potential and preparing future leaders of the state.

Your skills will make a difference.

You’ll be working for a university that is internationally recognized for research and the work you do will make a difference in the lives of our students, faculty and staff. If you’re the type of person that wants to know your work has meaning and impact, you’ll like working for our campus.

The University of Texas at Austin and Enterprise Technology provide an outstanding benefits package to our staff. Those benefits include:

  • 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 

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

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

Purpose

The Senior Data Engineer for the UT Data Hub improves university outcomes and advances the UT mission to transform lives for the benefit of society by increasing the useability and value of institutional data. You will create complex data pipelines into UT’s cloud data ecosystem in support of academic and administrative needs. In collaboration with our team of data professionals, you will help build and run a modern data hub to enable advanced data-driven decision making for UT. You will leverage your creativity to solve complex technical problems and build effective relationships through open communication.

Responsibilities

Data Engineering:

  • Lead the design, development, and automation of scalable, high-performance data pipelines across institutional systems, AWS, Databricks, and external vendor APIs. 
  • Implement Databricks Lakehouse architectures to unify structured and unstructured data, enabling AI-ready data platforms that support advanced analytics and machine learning use cases. 
  • Build robust and reusable ETL/ELT workflows using Databricks, Spark, Delta Lake, and Python to support batch and streaming integrations. 
  • Ensure performance, reliability, and data quality of data pipelines through proactive monitoring, optimization, and automated alerting. 
  • Partner with business and technical stakeholders to define and manage data pipeline parameters—including load frequency, transformation logic, and delivery mechanisms—ensuring alignment with analytical and AI goals. 
  • Ensure all data engineering solutions adhere to university security, compliance, and governance guidelines, while leveraging best practices in cloud-native data development. 
  • Develop and maintain comprehensive technical documentation of data pipeline designs, data flows, and operational procedures. 
  • Collaborate with enterprise data architects, data modelers, data stewards, and subject matter experts to ensure data consistency, lineage, and semantic alignment across the ecosystem. 
  • Continuously evaluate and introduce emerging technologies—such as Databricks Unity Catalog, MLflow, Delta Live Tables, and AI-driven data observability tools—to enhance the data engineering landscape. 
  • Drive innovation by modernizing existing pipelines toward AI-readiness, enabling future integration with predictive analytics and machine learning models. 
  • Stay current with advances in Databricks, AI-driven data engineering, and cloud technologies, and advocate for their responsible adoption. 
  • Contribute to the vision of building a modern, AI-ready data ecosystem that powers advanced analytics, automation, and decision intelligence across the University. 

Collaboration, Support, & Communication:

  • Work both independently and collaboratively within cross-functional teams to deliver data products and pipelines that meet the University’s evolving data and analytics needs. 
  • Communicate clearly and effectively with technical and non-technical stakeholders regarding project progress, risks, dependencies, and technical challenges. 
  • Promote collaboration and knowledge sharing within the Data Engineering team through brainstorming sessions, design reviews, and Databricks best-practice discussions. 
  • Foster a culture of learning and innovation, supporting team morale and professional growth. 
  • Provide mentorship and peer guidance to junior data engineers on data pipeline design, Databricks workflows, and coding best practices. 
  • Participate in change management processes to ensure transparency and coordination across teams during system enhancements or platform migrations.

Perform other related functions as assigned. 

Required Qualifications

  • Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience. 
  • At least two years of hands-on experience in Data Engineering using cloud-based platforms (AWS, Azure, or GCP) with emphasis on Databricks or Spark-based pipelines. 
  • Proven experience in designing, building, and automating scalable, production-grade data pipelines and integrations across multiple systems and APIs. 
  • Proficiency in Python and SQL, with demonstrated ability to write efficient, reusable, and maintainable code for data transformations and automation. 
  • Strong knowledge of ETL/ELT principles, data lakehouse architectures, and data quality monitoring. 
  • Experience implementing and maintaining CI/CD pipelines for data workflows using modern DevOps tools (e.g., GitHub Actions, Azure DevOps, Jenkins). 
  • Familiarity with data governance, security, and compliance practices within cloud environments. 
  • Strong analytical, troubleshooting, and performance optimization skills for large-scale distributed data systems. 
  • Excellent communication and collaboration skills to work effectively with technical and non-technical stakeholders. 
  • Demonstrated experience mentoring and guiding junior engineers or peers on technical projects. 

Equivalent combination of relevant education and experience may be substituted as appropriate.

Preferred Qualifications

  • Five or more years of experience in Data Engineering or related fields with increasing technical leadership responsibilities. 
  • Three or more years of experience developing and optimizing data pipelines on Databricks, including Delta Lake, Delta Live Tables, and Databricks Workflows. 
  • Experience designing AI-ready data architectures and integrating data workflows with machine learning and analytics environments. 
  • Experience with distributed data processing frameworks such as Spark, Kafka, or Flink. 
  • Databricks or AWS certifications (e.g., Databricks Certified Data Engineer Professional, AWS Solutions Architect, or AWS Data Analytics Specialty). 
  • Two or more years of experience in Agile software development environments, including use of tools such as JIRA, Confluence, or similar for issue tracking and project management. 
  • Hands-on experience with data orchestration tools (e.g., Airflow, Databricks Workflows, or AWS Step Functions). 
  • Exposure to data governance frameworks and AI/ML operations (MLOps) concepts such as MLflow or model monitoring. 
  • Demonstrated ability to lead or supervise small teams or project-based technical efforts. 
  • Passion for continuous learning and staying current with advancements in Databricks, cloud-based data engineering, and AI enablement. 

Salary Range

$115,000 - $124,968

Working Conditions

  • May work around standard office conditions
  • Repetitive use of a keyboard at a workstation
  • Use of manual dexterity (ex: using a mouse)

Work Shift

  • Monday – Friday 8am-5pm; Occasional nights or weekends may be required

Required Materials

  • Resume/CV
  • 3 work references with their contact information; at least one reference should be from a supervisor
  • Letter of interest

Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded.  Once your job application has been submitted, you cannot make changes.

Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.​

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Employment Eligibility:

Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.

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Retirement Plan Eligibility:

The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.

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Background Checks:

A criminal history background check will be required for finalist(s) under consideration for this position.

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Equal Opportunity Employer:

The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

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Pay Transparency:

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

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Employment Eligibility Verification:

If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form.  You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States.  Documents need to be presented no later than the third day of employment.  Failure to do so will result in loss of employment at the university.

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E-Verify:

The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university’s company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following:

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Compliance:

Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.

The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.