Oncology Data Engineer - Precision Medicine

What is the Long-Term Incentive Plan (LTIP)?

Long-Term Incentive Plan (LTIP): is an incentive program that typically vests over a three-year period and is tied to both individual performance and the operational success of Texas Oncology. Awards are discretionary and based on your position, performance, and potential for future career growth at Texas Oncology. Awards are reviewed and approved during the annual compensation review. LTIP awards are subject to your continued employment through the award payment date, and are governed by the written terms and conditions of the LTIP document.

What does the Oncology Data Engineer do?

The Oncology Data Engineer will support Precision Medicine's data delivery team, design and build robust data pipelines and implement new data architecture to support informatics decision-making. Leveraging deep understanding of ETL methodologies, and AI technologies, the Oncology Data Engineer will create scalable and efficient solutions using innovative technology, including SQL, OpenAI tools and large language models (LLMs). Supports and adheres to US Oncology Compliance Program, to include the Code of Ethics Business Standards.

Responsibilities

The essential duties and responsibilities (included but not limited to):

Data Delivery Support

Design, develop, and maintain robust ETL pipelines for large-scale data ingestion and transformation from various sources such as Electronic Medical Records (EMRs), lab interfaces, and data warehouses.
Support data science initiatives with SQL coding from various data warehouses.
Implement new data architecture, drawing inspiration from existing pipelines.
Optimize ETL workflows for performance and accuracy, ensuring seamless data integration.
AI and LLM Integration

Integrate AI functionalities into data platforms using OpenAI tools and LLMs.
Collaborate with AI teams to implement AI-driven solutions within the data pipeline.
Stay updated on the latest advancements in AI and LLM technologies to enhance platform capabilities.
Collaboration and Support

Collaborate with cross-functional teams to understand requirements and translate them into technical solutions.
Monitoring and Maintenance

Implement monitoring and alerting systems to proactively identify and resolve platform issues.
Perform regular maintenance, updates, and upgrades to cloud infrastructure and associated services.
Documentation and Best Practices

Maintain comprehensive documentation of system architectures, processes, and procedures.
Advocate for and implement best practices in cloud engineering, SQL coding, ETL processes, and AI integration.

Qualifications

The ideal candidate will have the following background and experience:

Education

Bachelor’s or master’s degree in computer science, engineering, or a related field.
Healthcare & Oncology Domain Knowledge

Understanding of oncology workflows and clinical data types
Familiarity with molecular/genomic data (e.g., NGS, variants, biomarkers)
Experience integrating laboratory, pathology, and molecular testing data
Knowledge of healthcare data standards (HL7, FHIR, ICD-10, LOINC, SNOMED)
Experience working with EHR data (e.g., IKMg1/IKMg2, Epic, Copia)

Experience

7–10 years of professional experience in data engineering with a focus on ETL processes
Minimum 3+ years of professional experience in data engineering in Healthcare.
Strong background in cloud platforms (e.g., AWS, Azure, GCP).
Experience with OpenAI tools and integrating AI functionalities, including LLMs, into data platforms.

Technical Skills

Strong scripting and automation skills (e.g., Python).
Strong experience with SQL required.
Experience with GitHub, Confluence, Jira preferred

Soft Skills

Excellent problem-solving abilities and attention to detail.
Effective communication and teamwork skills.
Ability to manage multiple priorities in a challenging environment.