Data Engineer III

About the role

This is a Data Engineer opportunity with a fast-growing US healthtech business that partners with employers to steer members towards higher-quality doctors and lower-cost care. The role sits in the Engineering team and offers broad ownership of the data pipelines and reusable datasets that serve Business Intelligence, Marketing and Data Science Research. The business analyses a very large volume of claims and clinical quality data, so the quality and reliability of what you build underpins decisions across the company. You’ll build, optimise and maintain the pipelines that power the business, define and build reusable datasets for analytics and research teams, and design and promote a federated data validation framework that catches inconsistencies early. Protecting user privacy and security is part of the job. The stack is modern and broad, including Python, SQL, Airflow, Snowflake, Postgres, dbt, Spark and Terraform on AWS. The ideal candidate will have 2+ years’ software or data engineering experience, strong SQL and Python, and a track record of building scalable pipelines with query optimisation and sound data modelling. Experience with orchestration tools such as Airflow and warehouses such as Snowflake is expected, while familiarity with healthcare or insurance data, HIPAA compliance and event-driven or medallion architectures would be advantageous. This is an in-office role in New York City, three days a week (Tuesday to Thursday), suited to an engineer with a high sense of ownership who enjoys teaching as well as learning. 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