About the Role
We are seeking a highly skilled Data Infrastructure Specialist to join our team. This is a critical role where you will design, build, and operate scalable data pipelines that process clinical encounter data, retrieve associated medical documents, and deliver validated information to machine learning systems for inference.
- Design, build, and operate scalable, event-driven data pipelines that support real-time clinical workflows.
- Integrate with electronic health record (EHR) systems using standards such as FHIR and HL7.
- Develop reliable systems to ingest, track, and update clinical events in real-time.
- E nsure downstream machine learning systems receive high-quality, structured data for inference and decision support.
- Establish observability, monitoring, and error handling for robust pipeline performance.
- Collaborate with machine learning engineers to align data flows with model needs.
- Continuously improve pipeline design to maximize scalability, resilience, and automation.
Required Skills and Qualifications
To be successful in this role, you will need :
A strong background in workflow orchestration and event-driven systems.Proficiency with relational databases and SQL as well as handling unstructured data.Experience operating in cloud environments and working with distributed, highly available systems.A track record of delivering data infrastructure that supports machine learning or analytics applications.Excellent communication and collaboration skills.Benefits
In addition to the opportunity to work on cutting-edge technology, you will also enjoy :
Knowledge of HIPAA and healthcare data security / privacy requirements.Familiarity with FHIR / H L 7 data modeling in production settings.Experience supporting machine learning inference pipelines in real-world applications.Others
This is a fantastic opportunity to join a dynamic team and contribute to the development of innovative solutions. If you are passionate about data engineering and want to make a real impact, we encourage you to apply.