Particle Health is revolutionizing healthcare data analytics and interoperability by unlocking the power of medical records in an intelligent platform that focuses healthcare back on the patient. The company aims to bridge fragmented data silos to enable healthcare innovators to access standardized, real-time patient data for use cases such as care coordination, risk stratification, patient identity management, and regulatory compliance.
Requirements
- 3+ years of experience in data engineering or a related role.
- Strong skills in Python and SQL. Experience with Spark or other distributed data processing frameworks.
- Experience building or maintaining data pipelines, ideally using modern data tools (e.g., Airflow, Prefect, dbt).
- Experience working with healthcare data (such as EHR, FHIR, HL7, or claims data) — or a strong interest in learning healthcare data systems.
- Experience working with data lake or warehouse technologies (e.g., Iceberg, Delta Lake, BigQuery).
- Exposure to data modeling concepts and performance optimization.
- Familiarity with healthcare standards such as CCDA, FHIR, or HL7v2.
Responsibilities
- Build, maintain, and optimize scalable data pipelines for ingestion, transformation, and enrichment.
- Build sophisticated data and analytics products.
- Support data quality, monitoring, and reliability across multiple healthcare datasets.
- Collaborate with product and engineering teams to design features that improve patient care.
- Help improve and evolve our data infrastructure to reduce manual work and improve efficiency.
- Partner closely with data scientists, product managers, and software engineers to understand data needs and deliver reliable solutions.
- Participate in code reviews, design discussions, and cross-functional projects.
Other
- Strong communication skills and comfort working with both technical and non-technical stakeholders.
- A proactive learner who enjoys tackling new challenges and growing in a fast-moving environment.
- This is a hybrid role based in New York City. The team works in-office two days per week (Tuesdays and Thursdays), so applicants should be within commuting distance and comfortable with in-person collaboration.
- Collaborative culture and curiosity are valued.
