Engineering Manager

This is an Engineering Manager (Data Platform) 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 leads the Data Platform Engineering team, which builds the reusable data abstractions and high-throughput infrastructure behind the company’s internal and external applications. It also carries a mandate to drive an AI-native engineering culture. You’ll manage the team and own the design and operation of systems that process complex healthcare data, set standards for data modelling, processing and maintainability, and stay close to the architecture. You’ll turn ambiguous business and data requirements into scoped, estimated work, run design reviews and root cause analyses, mentor engineers, and align priorities with Engineering and Product against company OKRs. The stack includes Python, Spark, dbt, Snowflake, Iceberg, Airflow, Kubernetes and AWS. The ideal candidate will have 5+ years’ hands-on engineering experience in big data and distributed systems, and 2+ years managing engineering teams. Experience with batch processing (Spark, EMR) and stream processing (Kafka or similar) is expected, along with exposure to tools such as Temporal, Iceberg, Glue and dbt. Healthcare data standards such as HIPAA and EDI would be advantageous. This is an in-office role in New York City, three days a week (Tuesday to Thursday), suited to a technical manager who wants to stay hands-on while scaling a data platform.

Requirements