Job Posting Title:

Data Engineer I

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Hiring Department:

Dell Medical School

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Position Open To:

All Applicants

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Weekly Scheduled Hours:

40

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FLSA Status:

Exempt from FLSA

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Earliest Start Date:

Immediately

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Position Duration:

Expected to Continue

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Location:

UT MAIN CAMPUS

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Job Details:

General Notes

The Data Engineer I designs, builds, and maintains scalable healthcare data solutions that support clinical, operational, research, and enterprise analytics initiatives. This role partners with data scientists, analysts, software engineers, and clinical informatics teams to develop secure, reliable, and high-performing data pipelines and infrastructure that enable data-driven decision-making across Dell Medical School and UT Health Austin.

Important Employment Information

This position is not eligible for employer-sponsored work authorization. Applicants requiring current or future visa sponsorship are not eligible for employment in this position.

Purpose

The Data Engineer I is responsible for designing, building, and optimizing healthcare data pipelines and supporting enterprise data infrastructure. This role collaborates with cross-functional teams to develop scalable data solutions, improve data accessibility, ensure data quality, and support analytics, reporting, clinical operations, research, and strategic decision-making across the organization.

Responsibilities

Designs and Maintains Data Pipelines

• Design, build, and maintain scalable data pipeline architecture supporting structured and unstructured healthcare data
• Assemble large, complex datasets that meet functional and non-functional business requirements
• Develop scalable ETL/ELT pipelines utilizing SQL and AWS big data technologies
• Optimize pipeline performance for scalability, latency, throughput, and fault tolerance
• Ensure data pipelines comply with HIPAA and organizational data governance standards

Develops and Manages Data Infrastructure

• Build infrastructure supporting extraction, transformation, and loading of data from diverse healthcare sources
• Develop and maintain enterprise data lakes, data warehouses, and data marts utilizing platforms such as Snowflake, Amazon Redshift, or Google BigQuery
• Configure cloud-based storage and compute environments across AWS, Azure, and Google Cloud Platform
• Implement schema design, indexing, partitioning, and infrastructure optimization strategies
• Support high availability, disaster recovery, and business continuity planning

Enables Analytics and Data Science

• Develop data tools supporting analytics, reporting, and data science initiatives
• Create reusable components supporting dashboards, reporting, and data products
• Build data models and curated datasets for analysts and data scientists
• Enable self-service analytics through standardized datasets and data models
• Collaborate with stakeholders to define key performance indicators (KPIs) and organizational metrics

Improves Internal Processes and Scalability

• Identify, design, and implement internal process improvements
• Automate manual processes and optimize enterprise data delivery
• Improve infrastructure scalability, performance, and maintainability
• Refactor legacy data solutions to improve efficiency
• Develop and support CI/CD pipelines for data engineering workflows

Collaborates Across Teams

• Partner with executive leadership, product teams, analysts, software engineers, data scientists, and clinical informatics teams to support enterprise data initiatives
• Translate business requirements into scalable technical solutions
• Support cross-functional projects and Agile development teams
• Communicate technical concepts effectively to both technical and non-technical stakeholders
• Mentor junior data engineering team members as appropriate

Ensures Data Governance and Security

• Support enterprise data governance, security, and regulatory compliance initiatives
• Implement data validation, anomaly detection, and data quality monitoring processes
• Collaborate with data governance teams to enforce organizational standards and policies
• Audit data for completeness, accuracy, consistency, and timeliness
• Support data stewardship and master data management initiatives

Marginal or Periodic Functions

• Conduct training sessions supportin