Remote Oncology Data Engineer - Precision Medicine - Dallas, Tx
Indexed description
Why work for us?
Come join our team that is responsible for helping lead Texas Oncology in treating more patient diagnosed with cancer than any other provider in Texas. We offer our employees a competitive benefits package that includes Medical, Dental, Vision, Life Insurance, Short-term and Long-term disability coverage, a generous PTO program, a 401k plan that comes with a company match, a Wellness program that rewards you practicing a healthy lifestyle, and lots of other great perks such as Tuition Reimbursement, an Employee Assistance program and discounts on some of your favorite retailers.
Join a Team That Invests in Your Future
At Texas Oncology, we recognize the long-term impact of our people and are committed to rewarding performance and potential. That’s why select roles may be eligible to participate in our Long-Term Incentive Plan (LTIP): an incentive program designed to attract, retain, and reward top talent.
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.
- 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.
- 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.
- Experience with GitHub, Confluence, Jira preferred
- Excellent problem-solving abilities and attention to detail.
- Effective communication and teamwork skills.
- Ability to manage multiple priorities in a challenging environment.
Work Environment
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations will be offered to enable individuals with disabilities to perform essential functions. The work environment is typical of an office setting.
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