Data Engineer

About the role

This is a Data Integration Engineer opportunity with a Series A US healthtech business whose AI platform gives hospitals end-to-end visibility into their supply chain. It is a customer-facing engineering role that connects hospital ERP, supply chain and operational systems to the platform and keeps those integrations running in production. You’ll run technical discovery sessions with hospital IT and supply chain teams, build and maintain production pipelines from customer systems into the platform, and troubleshoot live integration failures such as schema mismatches, authentication problems and upstream data quality issues. You’ll create reusable connectors and integration patterns to speed up onboarding, write documentation and runbooks, feed field learnings back to product and engineering, and give clear status and risk updates. The stack is Python, SQL, REST APIs, ETL pipelines and AWS, with HL7, FHIR, Epic, Cerner, Workday, Airflow, dbt and Snowflake as nice-to-haves. The ideal candidate will have 4+ years of professional software engineering experience, strong production Python and SQL, hands-on experience building production data pipelines or system integrations, and experience running technical calls with external customers. Experience with REST APIs, webhooks and SFTP is expected, and healthcare data standards, hospital operations and early-stage startup experience would be advantageous. This is a fully remote US position. We're looking for someone curious, pragmatic, and eager to work across the stack with a small senior team. You'll own end-to-end delivery of major features, influence architecture, and mentor engineers.

What you'll do

• Ship customer-facing features on a weekly cadence.
• Partner with design and product on scoping and trade-offs.
• Drive technical quality through review, testing, and docs.
• Mentor one or two engineers on your team.

Requirements