Senior Data Engineer

About the role

This is a Senior 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 Engineering, reports to the VP of Engineering, and owns the pipelines and reusable datasets that serve Business Intelligence, Marketing and Data Science Research. The business works with a very large volume of claims and clinical quality data, so the reliability of this work underpins decisions across the company. You’ll build, optimise and maintain data pipelines, define and build abstracted 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 broad and modern, including Python, SQL, Airflow, Snowflake, Spark, Iceberg, dbt and Terraform on AWS. The ideal candidate will have 4+ years’ software or data engineering experience across distributed data processing, warehousing, data quality and governance, with expert SQL and strong Python. Experience with Airflow, PostgreSQL, Iceberg and Snowflake, SQL tuning, Spark development, and medallion and event-driven architectures is expected. Familiarity with healthcare or insurance data and HIPAA would be advantageous. This is an in-office role in New York City, three days a week (Tuesday to Thursday), suited to a senior engineer who wants ownership of a core data foundation. 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