Remote Oncology Data Engineer - Precision Medicine - Dallas, Tx

The US Oncology Network•Dallas, TX

3d•Remote

About The Position

Texas Oncology is seeking a Remote Oncology Data Engineer to join their Precision Medicine team. This role, based out of the Dallas, Texas corporate office, is crucial for supporting the data delivery team. The engineer will be responsible for designing and building robust data pipelines, implementing new data architecture to support informatics decision-making, and leveraging expertise in ETL methodologies and AI technologies, including SQL, OpenAI tools, and large language models (LLMs), to create scalable and efficient solutions. The position also requires adherence to the US Oncology Compliance Program and Business Standards.

Requirements

Bachelor’s or master’s degree in computer science, engineering, or a related field.
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)
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.
Strong scripting and automation skills (e.g., Python).
Strong experience with SQL required.
Excellent problem-solving abilities and attention to detail.
Effective communication and teamwork skills.
Ability to manage multiple priorities in a challenging environment.

Nice To Haves

Experience with GitHub, Confluence, Jira preferred

Responsibilities

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.
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.
Collaborate with cross-functional teams to understand requirements and translate them into technical solutions.
Implement monitoring and alerting systems to proactively identify and resolve platform issues.
Perform regular maintenance, updates, and upgrades to cloud infrastructure and associated services.
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.