Senior Data Engineer
University of Texas - Austin · AUSTIN, TX ·
- Seniority
- Senior
- Employment
- Full time
- Category
- Data engineering
- Experience
- 6+ years
University of Texas - Austin · AUSTIN, TX ·
University of Texas at Austin · UT MAIN CAMPUS
University of Texas at Austin · UT MAIN CAMPUS
University of Texas at Austin · UT MAIN CAMPUS
Job Posting Title:
Senior Data Engineer----
Hiring Department:
Dell Medical School----
Position Open To:
All Applicants----
Weekly Scheduled Hours:
40----
FLSA Status:
Exempt from FLSA----
Earliest Start Date:
Immediately----
Position Duration:
Expected to Continue----
Location:
AUSTIN, TX----
Job Details:
The Senior Data Engineer is a highly experienced data professional responsible for leading the design, development, and optimization of complex data pipelines and platforms that support enterprise analytics, reporting, and advanced data use cases. This role serves as a technical leader within the data engineering team, owning moderately large initiatives, guiding architectural decisions, and mentoring Data Engineers.
Reporting to the Director of Data Intelligence and Decision Science (or a designated senior leader), the Senior Data Engineer partners closely with data scientists, analysts, software engineers, informaticists, and business stakeholders. The role ensures scalable, secure, and high-quality data solutions while supporting organizational priorities in clinical, operational, financial, and research domains. The Senior Data Engineer plays a key role in preparing the organization for advanced analytics, automation, and AI/ML adoption, without holding full enterprise-wide ownership reserved for the Principal Data Engineer.
Leads Design and Optimization of Data Pipelines
Designs, builds, and maintains complex, scalable ETL/ELT pipelines for structured and unstructured data.
Leads integration of data from EHRs, financial systems, registries, and external data sources.
Optimizes pipelines for performance, reliability, fault tolerance, and cost efficiency.
Implements batch and near–real-time data processing patterns as needed.
Ensures pipelines meet regulatory, privacy, and security requirements (e.g., HIPAA).
Owns Key Data Platforms and Architecture Components
Serves as technical owner for specific data platforms, domains, or subject areas (e.g., clinical analytics, operational reporting).
Designs and maintains data lake, warehouse, and data mart structures using cloud platforms
Develops and enforces data modeling standards, schema design, and partitioning strategies.
Partners with IT and cloud teams to ensure availability, scalability, and disaster recovery readiness.
Enables Advanced Analytics and Data Science
Builds curated, analytics-ready datasets and reusable data assets for analysts and data scientists.
Collaborates with data science teams to support feature engineering, model training, and deployment workflows.
Develops frameworks and patterns that improve self-service analytics and reduce ad hoc data requests.
Supports experimentation and proof-of-concept work for predictive analytics and AI/ML use cases.
Drives Process Improvement and Engineering Best Practices
Leads initiatives to improve data engineering workflows, including automation, monitoring, and CI/CD for data pipelines.
Refactors legacy pipelines and infrastructure to improve maintainability and scalability.
Establishes best practices for code quality, documentation, testing, and version control.
Evaluates new tools and technologies and recommends adoption where appropriate.
Mentors and Provides Technical Leadership
Serves as a technical mentor to Data Engineer staff.
Reviews code, pipeline designs, and architecture artifacts to ensure quality and consistency.
Provides guidance on complex technical problems and helps unblock team members.
Contributes to onboarding, internal training, and knowledge-sharing activities.
Collaborates with Stakeholders and Leads Medium-to-Large Initiatives
Partners with business, clinical, research, and operational stakeholders to translate requirements into technical solutions.
Leads data engineering workstreams within cross-functional projects or agile squads.
Communicates technical concepts, trade-offs, and risks to non-technical audiences.
Supports planning, estimation, and prioritization of data engineering initiatives.
Supports data integration efforts for new service lines, acquisitions, or system migrations.
Participates in vendor evaluations and technical assessments.
Assists with disaster recovery testing and business continuity planning.
Contributes to grant proposals or research initiatives requiring advanced data infrastructure.
Performs related duties as required.
Technical Expertise
Advanced proficiency in SQL and Python and related languages for data engineering.
Strong experience with distributed data processing frameworks (e.g., Spark).
Hands-on expertise with workflow orchestration tools (e.g., Airflow).
Deep familiarity with cloud-based data platforms and services (AWS, GCP, or Azure/Fabric).
Experience designing and optimizing data models for analytics and reporting.
Data Governance and Compliance
Strong understanding of data governance, data quality, and security best practices.
Experience working with regulated data, particularly healthcare or clinical data.
Familiarity with healthcare data standards (e.g., HL7, FHIR) preferred.
Problem Solving and Decision Making
Analyzes complex systems to identify root causes and scalable solutions.
Balances short-term delivery with long-term architectural sustainability.
Makes sound technical decisions with limited ambiguity.
Collaboration and Leadership
Effectively collaborates across technical and non-technical teams.
Provides constructive feedback and technical guidance to peers.
Demonstrates ownership, accountability, and initiative.
Bachelor’s Degree in Computer Science, Data Engineering, Information Systems, or a related field.
At least 6 years of experience in data engineering, analytics engineering, or data platform development.
Demonstrated experience designing and leading complex data pipelines and data platforms.
Relevant education and experience may be substituted as appropriate.
Master’s Degree in Data Science, Data Engineering Computer Science, Informatics, or related field.
Experience in healthcare data engineering or regulated data environments.
Exposure to AI/ML infrastructure, feature stores, or model operationalization.
Experience leading technical initiatives or acting as a team lead.
REQUIRED: None
Cloud Certification
Microsoft Certified: Azure Data Engineer Associate
Google Cloud Professional Data Engineer
AWS Certified Data Analytics – Specialty
$138,000+ depending on qualifications
Standard office equipment
Repetitive use of a keyboard
May be exposed to healthcare-related occupational hazards depending on assignment
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.----
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.
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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.
University of Texas at Austin · UT MAIN CAMPUS
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