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Lead Data EngineerThe University of Texas at Austin • Austin, Texas, United States
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Lead Data Engineer

The University of Texas at Austin
  • US
    Austin, Texas, United States
  • US
    Austin, Texas, United States
Jetzt Bewerben

Über

Lead Data Engineer
Enterprise Technology - Data to Insights (D2I)
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 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. 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.
Benefits
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 Lead 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 lead senior data engineers and data engineers to create complex data pipelines within 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.
Responsibilities
Technical Leadership:
Design, architect, and deliver production-grade, scalable data pipelines and AI-ready data platforms using Databricks, AWS cloud-native services and modern data engineering frameworks.
Lead end-to-end implementation of lakehouse data pipelines, ensuring performance, reliability, and cost efficiency.
Champion industry best practices for data engineering.
Conduct and participate in peer code reviews to maintain code quality and consistency across the team.
Proactively identify and resolve bottlenecks in data ingestion, transformation, and orchestration processes using Databricks Delta Live Tables, Spark optimization techniques, and workflow automation.
Implement systems for data quality, observability, governance, and compliance using tools such as Unity Catalog, Delta Lake, and data validation frameworks.
Lead technical knowledge-sharing sessions on topics such as AI/ML integration, data lakehouse architecture, and emerging data technologies.
Project Management:
Define project milestones, timelines, and deliverables for data and AI initiatives, ensuring timely and high-quality outcomes.
Collaborate with both internal and external stakeholders such as data architects, system architects, business users, Agile team members, and other D2I internal groups.
Manage project priorities, sprint planning, and team workloads while balancing innovation with delivery.
Communicate risks, dependencies, and resource constraints effectively, and develop mitigation plans for on-time project delivery.
Team Management and Leadership:
Supervise and mentor a team of Data Engineers (2–5 individuals) working on cloud, Databricks, and AI pipeline initiatives.
Foster a culture of continuous learning, experimentation, and technical excellence, encouraging engineers to explore AI and automation use cases.
Participate in recruiting, onboarding, and developing data engineering talent with strong Databricks and AI skillsets.
Conduct performance reviews, set development goals, and create individualized growth plans for team members.
Encourage collaboration across Data, AI/ML, Analytics, and Infrastructure teams to drive cross-functional success.
Communication:
Provide regular updates on project progress, technical challenges, and project milestones to both technical and business stakeholders.
Translate complex technical concepts related to Databricks, AI, and data architecture into clear narratives for non-technical audiences.
Foster a transparent communication culture and provide actionable feedback to promote a growth mindset.
Ensure all data engineering processes, architectures, and standards are well-documented for reuse, governance, and knowledge continuity.
Innovation and Other Duties:
Stay current with advancements in AI, data engineering, and Databricks ecosystem, evaluating new tools and frameworks for potential adoption.
Pilot and promote innovative solutions such as AI-assisted data quality checks, data observability automation, and intelligent pipeline optimization.
Perform other duties as assigned, contributing to the organization’s data-driven and AI-enabled transformation.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or equivalent professional experience.
5+ years of experience designing, implementing, and optimizing complex, production-grade data pipelines or enterprise-scale data platforms.
5 years of experience in cloud-based data engineering using Databricks and Amazon Web Services (AWS) (e.g., Glue, S3, Lambda, Redshift).
3+ years of experience managing or leading teams of data and/or software engineers, including mentorship, performance management, and project delivery.
Expertise in Python, PySpark, and SQL, with strong understanding of data modeling, stored procedures, and scalable data transformations.
Proven experience architecting and implementing ETL/ELT solutions across relational, non-relational, and lakehouse environments (e.g., Delta Lake, Parquet, or Iceberg).
Experience designing and managing CI/CD pipelines and infrastructure as code (IaC) using tools such as Databricks Repos, CDK, Terraform, or GitHub Actions.
Demonstrated knowledge of test-driven development (TDD) and data quality frameworks, ensuring reliability and reproducibility across data workflows.
Deep understanding of data governance, security, and compliance standards in cloud environments.
Excellent analytical, problem-solving, and debugging skills across distributed data systems.
Proven ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
Experience supervising, mentoring, and guiding junior team members on technical and professional development.
Equivalent combination of relevant education and experience may be substituted as appropriate.
Preferred Qualifications
8+ years of experience in Data Engineering or related fields, including 5+ years of hands‑on experience building and optimizing data pipelines on Databricks or similar large-scale data platforms.
Proven experience implementing lakehouse architectures leveraging Databricks Delta Lake, Delta Live Tables, and Unity Catalog for governance and scalability.
Experience designing AI‑ready data platforms and integrating machine learning pipelines using tools such as MLflow or model registry frameworks.
3+ years of experience managing or leading cross‑functional technical teams, fostering collaboration between Data Engineering, Analytics, and AI/ML teams.
5+ years of experience with Agile software development methodologies and project tracking systems such as JIRA.
Expertise in distributed data processing and streaming frameworks, such as Apache Spark, Kafka, Flink, or Airflow, for orchestration and automation.
Strong familiarity with data observability, cost optimization, and performance tuning in Databricks and cloud‑native architecture.
Professional certifications such as Databricks Certified Data Engineer Professional or AWS Solutions Architect or AWS Data Analytics Specialty are highly desirable.
Demonstrated ability to introduce new technologies and best practices to modernize existing data environments and promote AI/analytics maturity across the organization.
Passion for continuous learning and staying current with emerging technologies in data engineering, AI integration, and Databricks ecosystem advancements.
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 (e.g., 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.
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 uses 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 items:
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) and report to the relevant authorities. If this position is identified as 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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  • Austin, Texas, United States

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