Principal Data Engineer

Hims & Hers • ,

4d • $220,000 - $260,000 • Remote

About The Position

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 BigQuery, Airflow on Astronomer/EKS, dbt, Confluent Kafka, Databricks Delta Lake, and Terraform/OpenTofu. 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.

Requirements

• 12+ years building and owning data platform architecture, with a record of decisions whose consequences you stayed to live with, ideally across multiple companies or multiple platform generations
• Evidence of setting technical direction for organizations without direct management authority, achieving adoption through credibility
• Experience defining the boundaries of architectural authority and navigating ambiguity
• Deep GCP and BigQuery expertise, with operational fluency in AWS (for Airflow on EKS)
• Experience with modern data stack at platform scale, including governing dbt, Airflow/Astronomer, Kafka/Confluent, Databricks/Spark, Fivetran, and reverse ETL or activation platforms
• Experience with event streaming in production at scale, including Schema Registry, data contracts, consumer lag management, and delivery semantics, with an understanding of costs
• Experience with data governance and compliance in a regulated environment (HIPAA, PHI handling, data classification, access controls, audit logging, GDPR)
• Experience with Terraform or equivalent, and a belief that infrastructure changes are software changes
• Strong Python and SQL skills, with a focus on raising the bar for production pipeline code through reviews
• Writing skills that create alignment across organizations, producing design documents that serve as decision-making platforms

Nice To Haves

• Databricks, Unity Catalog, and Delta Lake in production at scale
• CDC patterns and Flink for real-time processing, especially where streaming infrastructure served both analytics and production or model serving paths across multiple teams
• PySpark and SparkSQL for large-scale batch and streaming workloads
• Leading a BigQuery to Databricks Lakehouse migration, or an equivalent warehouse migration end-to-end
• MLOps partnership, including model training pipelines, feature stores, experimentation infrastructure, or data foundations for AI-driven product features
• Go or Python service development for Kafka producers and consumers
• Direct-to-consumer healthcare or telehealth experience with HIPAA and GDPR obligations
• SOX compliance controls in a data engineering context

Responsibilities

• Define the multi-year platform architecture, including ingestion, streaming, orchestration, transformation, and serving as one system rather than five. Decide what to build, buy, deprecate, and in what order.
• Make the streaming bet: determine if Kafka to Flink to BigQuery remains an analytics pipeline or becomes production infrastructure, and understand the operational, financial, and organizational commitments.
• Define the contracts between organizations, including interfaces, service boundaries, and data contracts for how other teams consume from and write into the platform.
• Define what "trustworthy data" means, including quality and freshness guarantees, dataset tiering, and the accountability model for missed guarantees. Staff engineers will build detection, validation, and alerting systems.
• Define the economics of the platform, including design decisions impacting data costs (storage, compute, reservations, orchestration) and establish an accountability framework for consumption.
• Define the self-service strategy, identifying capabilities for Analytics Engineering and Data Science to operate independently while maintaining production stability and preventing the platform from becoming a bottleneck.
• Establish the standards for the data discipline, including testing, CI/CD, observability, schema governance, and infrastructure as code practices, ensuring adoption through quality rather than authority.
• Chair Architecture Review, serving as the decision-maker for cross-team, multi-system, and cost-impacting changes, including those affecting systems not directly owned.
• Manage the seams between data and other areas, including ML and Data Science readiness, HIPAA and PHI compliance with Legal and Security, and infrastructure hardening with DevOps.
• Create the written record, including ADRs, design docs, and RFCs, that serve as references for other teams.
• Elevate the technical ceiling of the organization through mentorship, design review, and hands-on pairing to empower Staff and Senior engineers.
• Remain hands-on by writing and reviewing production code and participating in platform-level P1 incidents, focusing on driving systemic fixes.

Benefits

• Competitive salary & equity compensation for full-time roles
• Unlimited PTO, company holidays, and quarterly mental health days
• Comprehensive health benefits including medical, dental & vision, and parental leave
• Employee Stock Purchase Program (ESPP)
• 401k benefits with employer matching contribution
• Offsite team retreats

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed