Senior/Staff Data Engineer

Role overview Own and improve data infrastructure that turns operational data into trusted insights for reporting, experimentation, and organizational decisions. This senior or staff-level role combines hands-on pipeline and warehouse engineering with technical guidance, cross-functional support, and a focus on reliability, performance, and governed access to data.

Responsibilities - Build and maintain ELT and streaming pipelines, emphasizing correctness, observability, and performance. - Orchestrate and monitor data workflows with tools such as Dagster or Airflow and dbt, including data quality checks. - Design and maintain well-modeled Snowflake warehouse areas, with clear definitions and lineage documentation. - Investigate urgent data issues, prevent downstream disruptions, and address performance bottlenecks. - Contribute to architecture and design decisions, and help stakeholders use data through analytics tools and AI agents. - Mentor teammates, document practices, share learnings, and participate in on-call support for pipeline health.

Requirements - A technical degree or equivalent practical experience. - At least five years in data, including four or more years in data engineering. - Strong analytical SQL skills, including CTEs, window functions, aggregation, and performance tuning. - Proficiency in Python and hands-on experience with a modern data stack. - Experience handling sensitive data in a regulated setting. - Ability to take loosely defined problems through to completion and communicate tradeoffs clearly. - Ability to commute within 75 minutes of the New York City office and work onsite four days per week.

Nice to have - Healthcare experience, Airflow migrations, Kafka Streams, or use of AI development tools.

Benefits and work setup - Target base salary is $175,000–$255,000 annually, with the final amount based on factors such as experience, expertise, and level. Additional compensation may include stock options and benefits for full-time employees.