The University of Texas at Arlington (UTA) Health Innovation Institute seeks to solve the problem of translating complex biomedical data into actionable insights by designing and operating robust, secure data pipelines for clinical-research products, analytics dashboards, and downstream data-science workloads.
Requirements
- Expert expertise in SQL (PostgreSQL, SQL Server, MySQL, etc.) and data-modeling (relational & dimensional).
- Proficiency in Python (or another modern language) for ETL, API integration, and automation.
- Experience with Snowflake, Microsoft Azure Synapse, or other modern data-warehouse platforms.
- Exposure to machine-learning pipelines (e.g., using OpenAI or other LLM services).
- Experience building/maintaining cloud data platforms (such as GCP, OCI, Linode, AWS, Azure) and data-lake/warehouse solutions, as well as production workload management.
- Hands-on Linux system administration (containerization, networking, security).
- Knowledge of healthcare data standards (Epic Clarity/Caboodle, HL7/FHIR, HPO, etc.) – preferred.
Responsibilities
- Architect end-to-end pipelines that ingest high-volume de-identified clinical, genomic and phenotypic datasets from collaborators’ EHR systems (Epic Clarity/Caboodle) and cloud storage.
- Build and host production-grade web portals and REST APIs for secure researcher/clinician access supporting role-based permissions and audit trails.
- Leverage OpenAI LLMs (or similar NLP services) to auto-extract Human Phenotype Ontology (HPO) terms from de-identified clinical documentation.
- Design high-throughput ETL workflows that parse heterogeneous datasets for ingestion into relational databases and cloud-native warehouses, feeding results into downstream analytics pipelines.
- Design and develop real-time capable analytical systems to integrate with and/or augment EHR systems.
- Perform systems administration for data-platform hosts, including system hardening, patch management, firewall configuration.
- Implement monitoring stacks and custom health checks to maintain near-continuous system availability.
Other
- Bachelor’s degree in Computer Science, Engineering or related field (or equivalent experience).
- Seven (7) years of professional experience in data engineering, software development, or an equivalent mix of education and relevant experience in similar role.
- Will engage with secure computing environments, IRB-protected datasets, and sensitive clinical data workflows.
- Office, computational, and occasional clinical–research interface settings.
- Applicants must include in their online resume the following information: 1) Employment history: name of company, period employed (from month/year to month/year), job title, summary of job duties and 2) Education: school name, degree type, and major.
