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. They are seeking a Principal Data Engineer to define and align the architectural vision for their data platform with business goals, working closely with product, engineering, and data science leadership.

Responsibilities:

• Own the long-term technical architecture for DPE across ingestion, orchestration, event streaming, and the platform infrastructure that enables transformation and serving - driving the highest-stakes decisions for the CDC-based streaming platform (Kafka → Flink → BigQuery), orchestration platform evaluation, and lower environment strategy
• Chair Architecture Review Committee (ARC) decisions; act as the primary technical DRI for cross-team, multi-system, and cost-impacting changes
• Establish and enforce engineering standards and production readiness criteria across all DPE-owned systems - testing requirements, CI/CD patterns, observability-as-code, logging standards, data contracts, Schema Registry governance, and what 'production-ready' means for emerging streaming and CDC capabilities
• Own data quality and observability architecture - dbt anomaly detection frameworks, schema validation, data drift alerting, and the platform standards that ensure consumers can trust the data they build on
• Define the technical strategy for self-service analytics: what platform capabilities enable Analytics Engineering to work independently, what guardrails prevent downstream breakage, and how DPE reduces its bottleneck over time
• Own evaluation, onboarding, and ongoing governance of DPE-managed tooling - Fivetran, Confluent, and equivalent platforms, including contract management, cost tracking, and deprecation decisions
• Own data sharing and egress patterns - access provisioning, cross-team data contracts, reverse ETL (Hightouch), and governed consumption paths for internal and external consumers
• Drive cost governance for platform infrastructure - BigQuery slot reservations, query optimization, partition strategies, orchestration rightsizing, and cloud spend accountability across the full DPE stack
• Lead incident response for platform-level P1/P2 incidents: act as technical escalation point, facilitate blameless RCAs, and drive systemic fixes that prevent recurrence
• Produce exemplary technical artifacts - architecture decision records, solution design docs, RFCs - that create alignment and become the team's reference standard
• Mentor and elevate Staff and Senior Data Engineers; raise the technical ceiling through design reviews, code reviews, and hands-on pairing
• Partner cross-functionally with ML/Data Science, legal/security/compliance, and DevOps to deliver platform capabilities that are ML-ready, compliant with HIPAA/GDPR, and hardened at the infrastructure layer
• Contribute hands-on to critical path work

Requirements:

• 15+ years of professional experience designing, building, and owning data platform architecture at company scale
• Demonstrated ability to define and align architectural vision with business goals across an entire engineering organization
• Deep expertise in cloud-native data platforms across GCP primary (BigQuery, GCS, Dataflow); AWS operational familiarity required (EKS-based Airflow) - BigQuery strongly preferred; multi-cloud fluency is required, not a plus
• Hands-on experience with the modern data stack: experience governing dbt at platform scale, Airflow/Astronomer, Kafka/Confluent, Databricks/Spark, Fivetran, and data sharing/activation platforms (Hightouch or equivalent)
• Experience designing and operating event streaming pipelines at scale - including Schema Registry, data contracts, and consumer lag management
• Proven track record establishing engineering standards across multiple teams and driving adoption without direct authority
• Experience owning data quality frameworks - dbt testing, anomaly detection, schema validation, and data observability tooling
• Experience with data governance and compliance frameworks in a regulated environment - HIPAA/PHI handling, data classification, access controls, audit logging, and GDPR
• Experience leading incident response for data platform outages - blameless RCA, systemic root cause identification, and operational improvement
• Infrastructure-as-code fluency - Terraform or equivalent; you treat infrastructure changes like software changes
• Strong Python and SQL skills; comfortable writing, reviewing, and raising the bar on production-grade pipeline code
• Clear written communication: you produce design docs and RFCs that build alignment, not confusion. Comfort operating in ambiguity - you define the path, you don't wait for it to be defined
• Databricks, Unity Catalog, and Delta Lake in production at scale
• Experience with CDC (Change Data Capture) patterns and Flink for real-time data processing
• PySpark/SparkSQL for large-scale batch and streaming workloads
• Experience driving a BigQuery → Databricks Lakehouse migration or equivalent cloud data warehouse migration
• Experience with MLOps - partnering with ML engineers on model training pipelines, feature stores, and experimentation infrastructure
• Go or Python service development for Kafka producers and consumers
• Experience at a direct-to-consumer healthcare or telehealth company with HIPAA and GDPR obligations
• Familiarity with SOX compliance controls in a data engineering context