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Principal Data Engineer
Principal Data EngineerUniversity of Texas at Austin • Austin, TX, United States
Principal Data Engineer

Principal Data Engineer

University of Texas at Austin • Austin, TX, United States
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  • [job_card.full_time]
  • [job_card.temporary]
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Job Posting Title :

Principal Data Engineer

Hiring Department :

Enterprise Technology - Data to Insights (D2I)

Position Open To : All Applicants

Weekly Scheduled Hours :

40

FLSA Status : Exempt

Earliest Start Date :

Immediately

Position Duration :

Expected to Continue Until Dec 19, 2026

Location : Texas

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 Principal 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 be responsible for leading the data engineering team to innovate and implement the newest data engineering trends and best practices to create complex data pipelines withing 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 within the team and outside partners.

This particular position has a heavy emphasis on Databricks and AI Readiness.

Responsibilities

Technical Leadership :

  • Architect, design, and lead the development of enterprise-scale, production-grade data platforms and pipelines using Databricks and cloud-native technologies (AWS, Azure, or GCP).
  • Champion the adoption of the Databricks Lakehouse architecture to unify data warehousing, data science, and machine learning workloads across the organization.
  • Guidethe design and deployment of AI-ready data pipelines to support predictive analytics, generative AI, and advanced decision intelligence use cases.
  • Define and enforce data engineering standards, including performance optimization, scalability, data observability, and cost efficiency.
  • Oversee code reviews, architecture reviews, and system design discussions to ensure technical excellence and maintainability across the engineering team.
  • Lead the implementation of robust data quality, governance, and compliance frameworks, leveraging Databricks Unity Catalog and modern metadata management tools.
  • Solve complex data architecture and integration challenges using advanced technologies such as Spark, Delta Live Tables, Airflow, andMLflow.
  • Drive the development of automated, CI / CD-enabled data workflows and promote best practices in datainfrastructureas code (IaC) and DevOps for data.

Project Management & Collaboration :

  • Provide strategic technical leadership and mentorship to data engineering teams, fostering a collaborative environment that promotes innovation, accountability, and growth.
  • Collaborate closely with data architects, AI / ML engineers, and analytics teams to align data solutions with organizational goals and research initiatives.
  • Engage with cross-campus and cross-departmental technical groups to evangelize modern data practices and accelerate AI transformation initiatives.
  • Lead knowledge-sharing sessions and architecture reviews on emerging data engineering trends, Databricks advancements, and AI integration techniques.
  • Communication :

  • Effectively communicate technical strategies, project status, risks, and architecture decisions to both technical and non-technical stakeholders.
  • Translate complex data engineering concepts into clear business impacts, helping decision-makers understand opportunities and trade-offs.
  • Produce clear and detailed technical documentation, design specifications, and operational playbooks to support long-term scalability and training.
  • Advocate for data engineering as a foundational enabler of AI, analytics, and digital transformation initiatives across the institution.
  • Innovation :

  • Lead research and development efforts to evaluate and implementcutting-edgetechnologies within the Databricks ecosystem and broader AI / data landscape.
  • Conduct feasibility studies and proofs of concept (POCs) for next-generation architectures involving AI model integration, real-time streaming, and intelligent automation.
  • Partner with academic,administrative,andcampusstakeholders to pilot AI-enabled data systems, such as model-assisted data validation and automated feature generation.
  • Stay ahead of emerging trends in data engineering, AI readiness, and cloud infrastructure, continuously recommending and implementing innovative solutions.
  • Other :

  • Contributeto recruitment, hiring, and onboarding of new data engineering team members.
  • Represent the data engineering function in strategic planning discussions and cross-organizational technology initiatives.
  • Perform other duties as assigned, aligned with the mission to build a secure, scalable, and AI-enabled data ecosystem.
  • Required Qualifications

  • Bachelor's orMaster's degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
  • 5+ years of experience designing, implementing, andmaintainingcomplex, production-grade data pipelines and enterprise data platforms.
  • 5+ years of hands-on experience with cloud-based data engineering, preferably in Amazon Web Services (AWS), withstrongcommand of services such as Glue, S3, Lambda, Redshift, and EMR.
  • 3+ years of experience defining cloud data architecture and data strategy in large, distributed enterprise environments.
  • Deepexpertisewith Databricks Lakehouse Platform, including Delta Lake, Delta Live Tables, and Unity Catalog, for scalable data ingestion, transformation, and governance.
  • Proficiencyin Python,PySpark, and SQL, withdemonstratedexperience in building ETL / ELT workflows across structured and unstructured data sources.
  • Proven ability to design and implement high-performance, AI-ready data architectures supporting analytics, machine learning, and real-time data processing.
  • Experience developing and deploying Continuous Integration / Continuous Delivery (CI / CD) pipelines for data engineering using tools such as Databricks Repos, GitHub Actions, or Terraform.
  • Strong foundationin test-driven data engineering, including automated data quality, validation, and observability frameworks.
  • Advanced knowledge of data governance, metadata management, and security compliance in cloud and Databricks environments.
  • Excellent systems analysis, design, and troubleshooting skills with the ability to address performance bottlenecks in distributed data systems.
  • Exceptional communication skills, with the ability to convey complex technical concepts clearly to both technical and non-technical stakeholders.
  • Proven experience leading and mentoring teams, fostering technical excellence and innovation.
  • Self-motivated and capable of working independently in a dynamic, evolving technology landscape.
  • Equivalent combination of relevant education and experience may be substituted as appropriate.

    Preferred Qualifications

  • 10+ years of experience in Data Engineering, Data Architecture, or related fields, including 5+ years of hands-on work with Databricks or equivalent large-scale data platforms.
  • Demonstrated experience architecting andoptimizingLakehouse environments that integrate data science, analytics, and AI workloads.
  • Proven success in implementing AI / ML-ready data pipelines and collaborating with Data Scientists andMLOpsteams using tools likeMLflow, Feature Store, or model registries.
  • 5+ years of experience applying Agile software development methodologies and using tools such as JIRA, Confluence, or Azure DevOps for project tracking and delivery.
  • Expertisein distributed data processing and streaming technologies such as Apache Spark, Kafka, Flink, or Airflow for orchestration and automation.
  • Experience designing and operationalizing data observability and cost optimization strategies within Databricks and cloud environments.
  • Strong understanding of data mesh, data fabric, and modern metadata management principles for large-scale organizations.
  • Professional certifications such as Databricks Certified Data Engineer Professional, AWS Solutions Architect, or AWS Data Analytics Specialty are highly desirable.
  • Demonstrated ability to drive innovation, introduce emerging technologies, and lead proofs of concept (POCs) for AI integration, automation, or advanced analytics.
  • Commitment to continuous learning and technology leadership, staying current with advancements in Databricks, AI engineering, and modern cloud data ecosystems.
  • Salary Range

    $125,000 - $143,712

    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 thatALLRequired 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 questionspresented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above.

    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.

    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.

    Background Checks :

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

    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.

    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.

    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.

    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 :

  • E-Verify Poster (English and Spanish) [PDF]
  • Right to Work Poster (English) [PDF]
  • Right to Work Poster (Spanish) [PDF]
  • 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.

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    Principal Data Engineer • Austin, TX, United States

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