Principal Data Engineer

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  and  . For information on the company’s outstanding benefits, culture, and its talent-first flexible/remote work approach, see below and visit

We’re hiring a Principal Data Engineer to set the technical direction for data at Hims & Hers. This is the most senior individual contributor role in Data Platform Engineering (DPE), and the scope is deliberately larger than DPE: the architecture you define determines how Software Engineering, Analytics Engineering, and Data Science build on data for millions of patients across our Telehealth, prescription, and wellness products.

The platform runs on GCP Big Query, Airflow on Astronomer/EKS, dbt, Confluent Kafka, Databricks Delta Lake, and Terraform/Open Tofu. Two foundational pieces are being built from scratch right now: a streaming platform and a lower environments strategy. The most consequential open question is whether streaming stays scoped to analytics or extends into production and model evaluation paths that other teams depend on.

That call is yours to make, and the company will live with it for years.

We have Staff engineers who own domains and ship them well. What we don’t have is a single person accountable for whether those domains add up to a coherent platform and for whether the bets we’re placing now still look right in five years. That’s this role.

• The multi-year platform architecture. Ingestion, streaming, orchestration, transformation, and serving as one system rather than five. You decide what we build, what we buy, what we deprecate, and in what order.

• The streaming bet. Whether Kafka to Flink to Big Query stays an analytics pipeline or becomes production infrastructure, and what that commits us to operationally, financially, and organizationally. You are the architect of the answer you choose.

• The contracts between organizations. The interfaces, service boundaries, and data contracts govern how other teams consume from and write into the platform. Where your standards end and another org’s autonomy begins is a boundary you define with the VP of Data, not one handed to you.

• What “trustworthy data” means here, and who is on the hook for it. You define the quality and freshness guarantees the platform makes, the tiering that determines which datasets get them, and the accountability model when they’re missed. Staff engineers build the detection, validation, and alerting systems. You decide what we promise, to whom, and at what cost.

• The economics of the platform. The design decisions that determine what data costs us at scale (storage and compute strategy, reservation model, orchestration footprint) and the accountability framework that keeps consumption honest as more teams build on it. You set the framework. You don’t tune every query.

• The self service strategy. What capabilities let Analytics Engineering and Data Science move without waiting on DPE, what guardrails keep that from breaking production, and how the platform stops being a bottleneck as demand grows faster than headcount.

• The standards the discipline runs on. Testing, CI/CD,…