About Our Ideal Data Engineer
* Collect and ingest large volumes of real-world data, including medical imaging and related annotations.
* Perform detailed quality checks to ensure accuracy, completeness, and consistency.
* Build and maintain data pipelines to structure and prepare complex datasets.
* Clean, transform, and normalize raw data for downstream research analysis.
* Work closely with Data Scientists to prepare specific curated subsets for focused analysis.
* Document data sources, processing steps, and quality control procedures.
Requirements:
* 3–7 years of experience as a Data Engineer or similar role.
* Strong proficiency in Python and SQL (required).
* Experience working with large-scale data pipelines.
* Clear and confident communication skills in English (written and verbal).
* Ability to work independently and manage multiple data workflows in parallel.
* Self-motivated and eager to work in a fast-paced, research-oriented environment.
Nice to Have:
* Experience with medical data, imaging (DICOM), clinical research, or pharmaceutical environments.
About the Role
This is an opportunity for a Data Engineer to join a global clinical research initiative. The role will support the full lifecycle of real-world clinical and imaging data, from acquisition and quality control to processing and organization, helping to enable data-driven insights for high-impact research programs.
Key Responsibilities:
The successful candidate will collect and ingest large volumes of real-world data, perform detailed quality checks, build and maintain data pipelines, clean and transform raw data, and work closely with Data Scientists. They will also document data sources, processing steps, and quality control procedures.
What You'll Need:
To be successful in this role, you'll need 3–7 years of experience as a Data Engineer or similar role, strong proficiency in Python and SQL, experience working with large-scale data pipelines, clear and confident communication skills in English, ability to work independently, and self-motivation.
Nice to Have:
Experience with medical data, imaging (DICOM), clinical research, or pharmaceutical environments would be beneficial but not required.