Staff 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. They are seeking a Staff Data Engineer to join the Data Platform Engineering team, responsible for driving architectural decisions and improving developer experience while building and maintaining critical data infrastructure.

Responsibilities:

• 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
• Own the reliability of systems you build: establish KPIs and SLOs, implement Datadog monitoring and alerting as code, participate in the on-call rotation, and own Tier 1 operational tickets and runbooks for systems under your domain
• Own the integration and data activation layer - Fivetran connectors and Hightouch reverse ETL pipeline connectors - end-to-end from IaC provisioning to production monitoring and schema change governance
• Support Analytics Engineers, Data Scientists, and ML engineers by building platform capabilities and data pipelines that unblock their roadmap; partner with legal, security, and DevOps on compliance controls and IaC hardening as needed. DE's responsibility is the platform layer and data delivery; transformation logic and model readiness for serving are owned by Analytics Engineering
• Identify and resolve systemic inefficiencies across DPE-owned pipelines and infrastructure - root cause, not just symptom
• Mentor Senior Data Engineers through design reviews, code reviews, and pairing; help them grow from squad-level to cross-squad scope
• Contribute to and drive adoption of engineering standards - testing practices, CI/CD patterns, observability-as-code, Schema Registry governance - and participate in ARC reviews for changes with cross-team or cost impact

Requirements:

• 8+ years of professional experience designing, building, and operating data pipelines and platform infrastructure
• Experience with CDC (Change Data Capture) patterns for real-time ingestion
• Experience with Flink for stream processing
• Experience governing and administering dbt in a production BigQuery or Databricks environment - CI/CD configuration, testing standards, documentation standards, and platform-level schema governance. Hands-on dbt experience for ingestion-layer (Bronze/Silver) pipelines
• Experience building and operating Airflow DAGs at scale - task-level orchestration patterns, DAG reliability, and multi-priority scheduling
• Experience building event streaming pipelines using Kafka or Confluent Kafka - producers, consumers, schema evolution, Schema Registry governance, and consumer lag management
• Multi-cloud fluency across GCP and AWS - both are required day-to-day: BigQuery runs on GCP, Airflow runs on AWS EKS
• Experience owning data quality for production pipelines - dbt tests, anomaly detection, alerting on schema changes and data drift
• Experience with Fivetran or equivalent connector platform - IaC provisioning, schema change handling, and connector health monitoring
• Experience with the Databricks platform - Delta Lake, Databricks Workflows, and Unity Catalog
• Familiarity with data compliance in a regulated environment - HIPAA/PHI handling, access controls, and audit logging
• Infrastructure-as-code experience - Terraform or equivalent; you treat infrastructure changes like code changes
• Strong Python and SQL skills; comfortable writing, reviewing, and raising the bar on production-grade pipeline code
• Strong design instincts: you take ambiguous requirements, write clear solution designs, and ship to production with minimal rework
• PySpark/SparkSQL for large-scale data processing
• Experience with Hightouch or equivalent reverse ETL platform
• Experience with MLOps - supporting ML engineers with data pipelines for model training, feature stores, or experimentation
• Familiarity with Looker LookML or equivalent BI serving layer
• Go experience for Kafka service development
• Experience at a direct-to-consumer healthcare, telehealth, or similarly regulated company
• Familiarity with UK/GDPR data compliance requirements distinct from US HIPAA