Data Engineer I

University of Texas at Austin

2d•Hybrid

About The Position

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. 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.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Statistics, or a related field
Minimum of two years of experience in data engineering, data architecture, ETL/ELT development, or a related technical discipline
Proficiency with big data technologies such as Hadoop, Spark, Kafka, or similar platforms
Experience working with both SQL and NoSQL databases
Experience developing and managing data pipelines and workflow orchestration tools
Experience with AWS services such as EC2, EMR, RDS, Redshift, Glue, and DynamoDB
Programming or scripting experience using Python, Java, C++, Scala, or similar languages
Strong analytical, troubleshooting, and problem-solving skills
Ability to collaborate effectively with cross-functional technical and business teams
Strong written and verbal communication skills
Relevant education and experience may be substituted as appropriate.

Nice To Haves

Master’s degree in Data Engineering, Computer Science, or a related field
Minimum of five years of experience in healthcare data engineering, analytics, or enterprise data architecture
Advanced SQL development and relational database experience
Experience designing, building, and optimizing enterprise data pipelines using Python
Experience with metadata management, workload orchestration, and data transformation frameworks
Knowledge of message queuing, stream processing, and scalable cloud-based data storage architectures
Experience supporting healthcare analytics, clinical data, and enterprise reporting initiatives
Strong project management and organizational skills
AWS Certified Data Analytics
Certified Health Data Analyst (CHDA)
Project Management Professional (PMP) Certification

Responsibilities

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
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
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
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
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
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
Conduct training sessions supporting enterprise data tools and platforms
Participate in vendor evaluations and proof-of-concept initiatives
Support data integration activities for organizational growth initiatives
Assist with disaster recovery exercises and business continuity planning
Support grant-funded research initiatives requiring enterprise data support
Perform related duties as assigned