Staff Data Engineer - Hims And Hers

Hims & Hers is the leading health and wellness platform, on a mission to help the world feel great through the power of better health. We are redefining healthcare by putting the customer first and delivering access to care that is affordable, accessible, and personal, from diagnosis to treatment to delivery.

No two people are the same, so we provide access to personalized care designed for results. By normalizing health & wellness challenges and innovating on their solutions, we’re making better health outcomes easier to achieve.

Hims & Hers is a public company, traded on the NYSE under the ticker symbol “HIMS.” To learn more about the brand and offerings, you can visit hims.com/about http://hims.com/about and hims.com/how-it-works http://hims.com/how-it-works . For information on the company’s outstanding

ABOUT THE ROLE

We're looking for a Staff Data Engineer to join the Data Platform Engineering team at Hims & Hers as a key technical driver for our most critical platform initiatives. Your scope spans multiple squads: you will drive shared architectural decisions, enhance cross-team reliability, and improve the overall developer experience for a team of nine engineers building the infrastructure that powers patient care for millions of Hims & Hers subscribers.

This is a hands-on execution role. You will own large, complex deliverables end-to-end - from design through production - across our full stack: BigQuery, dbt, Airflow on Astronomer, Confluent Kafka, Databricks, Fivetran, and Terraform/OpenTofu. You will be the DRI (Directly Responsible Individual) for cross-squad initiatives and the engineer other Senior DEs look to for technical direction and growth.

YOU WILL:

• Serve as DRI for high-complexity, multi-sprint platform initiatives - Fivetran connector buildouts, Databricks Lakehouse migration workstreams, event streaming infrastructure, lower environment implementation, and engineering standards adoption
• Architect, build, and maintain production-grade ingestion pipelines and platform infrastructure - from source connectivity through Bronze/Silver layers - that Analytics Engineering, Data Science, and business teams build on daily
• Design, implement, and operate event-driven and streaming data pipelines using Kafka, PySpark, and Databricks Structured Streaming - including defining scaling strategies, cost guardrails, consumer lag alerting, and runbooks before those services reach production
• Own the ingestion and raw-to-cleansed layer (Bronze to Silver) data contracts, schema governance, and SLAs
• Own data quality for pipelines you build: write dbt tests, wire anomaly detection, validate schemas, and alert on data drift - pipelines ship with quality gates, not after them