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# TELECOMMUTE Staff Data Engineer

- **Company:** Hims, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Data Analysis, Audit Trail, Big Data, BigQuery, Software Documentation, Code Review, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Infrastructure, Extract Transform Load (ETL), DevOps, Python (Programming Language), Standard Sql, Service Development Studio, Data Streaming, Google Cloud, Data Ingestion, Apache Spark, Multi-Cloud, Reliability of Systems, Change Data Capture, Data Lakes, Pyspark, Apache Flink, Maintaining Code, Apache Kafka, Machine Learning Operations, Data Delivery, Terraform, Stream Processing, Looker Analytics, Data Pipelines, AWS EKS, Confluent, Databricks
- **Published:** July 29, 2026
- **Apply:** https://www.dice.com/job-detail/a2687e7d-cf2a-4a1c-8e51-ddeee77cbbdb

## About the Role

* 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 Google Cloud Platform and AWS - both are required day-to-day: BigQuery runs on Google Cloud Platform, 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

## Description

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
 * 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


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