Senior Data Engineer

University of Texas at Austin•Austin, TX

21h•$138,000•Onsite

About The Position

The Senior Data Engineer is a highly experienced data professional who leads the design, development, and optimization of complex data pipelines and platforms supporting enterprise analytics, reporting, and advanced data use cases. The role serves as a technical leader on the data engineering team, owns moderately large initiatives, guides architectural decisions, and mentors Data Engineers. It reports to the Director of Data Intelligence and Decision Science or a designated senior leader and works closely with data scientists, analysts, software engineers, informaticists, and business stakeholders. The role develops scalable, secure, high-quality data solutions across clinical, operational, financial, and research domains and helps prepare the organization for advanced analytics, automation, and AI/ML adoption. The position leads the design and optimization of ETL/ELT pipelines; owns specific data platforms, domains, or architecture components; enables advanced analytics and data science; improves engineering processes and standards; provides technical mentorship; and leads medium-to-large cross-functional data engineering initiatives. It may also support integrations for new service lines, acquisitions, or migrations; vendor assessments; disaster recovery and continuity planning; and research initiatives requiring advanced data infrastructure. The position is based in Austin, Texas, with 40 scheduled hours per week. The salary range starts at $138,000, depending on qualifications. Standard office equipment is used; work involves repetitive keyboard use and may involve exposure to healthcare-related occupational hazards depending on assignment.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field; relevant education and experience may be substituted as appropriate.
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.
Advanced proficiency in SQL and Python and related languages for data engineering.
Strong experience with distributed data processing frameworks such as Spark.
Hands-on expertise with workflow orchestration tools such as Airflow.
Deep familiarity with cloud-based data platforms and services such as AWS, GCP, or Azure/Fabric.
Experience designing and optimizing data models for analytics and reporting.
Strong understanding of data governance, data quality, and security best practices.
Experience working with regulated data, particularly healthcare or clinical data.
Ability to analyze complex systems, identify root causes and scalable solutions, balance short-term delivery with long-term architectural sustainability, and make sound technical decisions with limited ambiguity.
Ability to collaborate across technical and non-technical teams, provide constructive feedback and technical guidance, and demonstrate ownership, accountability, and initiative.

Nice To Haves

Master’s degree in Data Science, Data Engineering, Computer Science, Informatics, or a related field.
Experience in healthcare data engineering or regulated data environments.
Familiarity with healthcare data standards such as HL7 and FHIR.
Exposure to AI/ML infrastructure, feature stores, or model operationalization.
Experience leading technical initiatives or acting as a team lead.
Cloud certification, such as Microsoft Certified: Azure Data Engineer Associate, Google Cloud Professional Data Engineer, or AWS Certified Data Analytics – Specialty.

Responsibilities

Design, build, maintain, and optimize complex, scalable ETL/ELT pipelines for structured and unstructured data.
Lead integration of data from EHRs, financial systems, registries, and external sources.
Optimize pipelines for performance, reliability, fault tolerance, and cost efficiency, and implement batch and near-real-time processing patterns as needed.
Ensure pipelines meet regulatory, privacy, and security requirements, including HIPAA.
Serve as technical owner for specific data platforms, domains, or subject areas.
Design and maintain data lake, warehouse, and data mart structures using cloud platforms.
Develop and enforce data modeling standards, schema design, and partitioning strategies; partner with IT and cloud teams on availability, scalability, and disaster recovery readiness.
Build curated, analytics-ready datasets and reusable data assets for analysts and data scientists.
Collaborate with data science teams on feature engineering, model training, and deployment workflows; support experimentation and proof-of-concept work for predictive analytics and AI/ML use cases.
Develop frameworks and patterns that improve self-service analytics and reduce ad hoc data requests.
Lead improvements to data engineering workflows, including automation, monitoring, and CI/CD for data pipelines.
Refactor legacy pipelines and infrastructure; establish best practices for code quality, documentation, testing, and version control; evaluate tools and recommend adoption.
Mentor Data Engineer staff, review code and architecture artifacts, guide resolution of complex technical problems, and contribute to onboarding, training, and knowledge sharing.
Partner with business, clinical, research, and operational stakeholders to translate requirements into technical solutions.
Lead data engineering workstreams within cross-functional projects or agile squads; communicate technical concepts, trade-offs, and risks to non-technical audiences.
Support planning, estimation, and prioritization of data engineering initiatives.
Support data integration for new service lines, acquisitions, or system migrations; participate in vendor evaluations and technical assessments.
Assist with disaster recovery testing and business continuity planning.
Contribute to grant proposals or research initiatives requiring advanced data infrastructure.
Perform related duties as required.