Description:

The Data Engineer will be responsible for designing, developing, and maintaining robust data ingestion pipelines to support AI-driven clinical trial solutions.
They will work with tools such as dbt, Apache Airflow, Docker, and Kubernetes to ensure scalable and reliable data workflows.
Collaboration with cross-functional teams and integration of data engineering solutions with machine learning models will enhance patient recruiting efficiency and precision.

Requirements:

3-5 years of production experience in data engineering roles, open to both senior and junior candidates.
Proficiency with Python Data Engineering stack.
Hands-on experience with dbt for data transformation.
Experience with Apache Airflow or Cloud Composer for workflow orchestration.
Proficiency with Docker for containerization and Kubernetes for orchestration.
Experience utilizing CI/CD and Infrastructure-as-Code (IaC) tools in a production environment.
Interest or experience in machine learning, LLMs, and Natural Language Processing (NLP) is highly desirable.
Strong understanding of data architecture and data modeling principles.
Familiarity with GitHub for version control and GCP for cloud deployments.

Opportunity to work with cutting-edge AI technology in the clinical trial landscape.
Flexible work hours with a 4-hour overlap in CST.
Independent Contractor contract type offering agility in research.
Chance to collaborate with cross-functional teams including data scientists, software engineers, and clinical experts.
Continuous learning and growth by staying updated with the latest trends and best practices in data engineering.