Principal Data Engineer

Hims & Hers as a Principal Data Engineer, where you will define and align the architectural vision for our data platform with business goals. You will set technical direction across every surface DPE owns, drive the highest-stakes architectural decisions, and establish the standards that the entire data engineering discipline operates by. Your scope will be org-wide, and you will own the long-term technical architecture for DPE across ingestion, orchestration, event streaming, and platform infrastructure. You will also lead incident response for platform-level P1/P2 incidents and mentor and elevate Staff and Senior Data Engineers.

Definir y alinear la visión arquitectónica de la plataforma de datos con los objetivos comerciales, estableciendo la dirección técnica y tomando decisiones arquitectónicas de alto nivel.

Ser responsable de la arquitectura técnica a largo plazo para la ingeniería de datos, incluyendo la ingesta, orquestación, transmisión de eventos y la infraestructura de la plataforma.

Responsibilities

• Establecer y hacer cumplir los estándares de ingeniería y los criterios de preparación para la producción en todos los sistemas de propiedad de DPE, incluyendo la calidad de los datos y la arquitectura de observabilidad.
• Experience owning data quality frameworks - dbt testing, anomaly detection, schema validation, and data observability tooling

Requirements

• 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
• 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)
• Demonstrated ability to define and align architectural vision with business goals across an entire engineering organization
• 15+ years of professional experience designing, building, and owning data platform architecture at company scale
• Experience designing and operating event streaming pipelines at scale - including Schema Registry, data contracts, and consumer lag management
• Infrastructure-as-code fluency - Terraform or equivalent; you treat infrastructure changes like software changes
• Proven track record establishing engineering standards across multiple teams and driving adoption without direct authority
• Strong Python and SQL skills; comfortable writing, reviewing, and raising the bar on production-grade pipeline code
• Experience with MLOps - partnering with ML engineers on model training pipelines, feature stores, and experimentation infrastructure
• Databricks, Unity Catalog, and Delta Lake in production at scale
• Go or Python service development for Kafka producers and consumers

Preferred

• 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
• 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
• Experience driving a BigQuery → Databricks Lakehouse migration or equivalent cloud data warehouse migration
• Familiarity with SOX compliance controls in a data engineering context
• Experience at a direct-to-consumer healthcare or telehealth company with HIPAA and GDPR obligations
• Experience with CDC (Change Data Capture) patterns and Flink for real-time data processing

Pay

USD 220,000 - 260,000

We are committed to building a workforce that reflects diverse perspectives and prioritizes ethics, wellness, and a strong sense of belonging. If you're excited about this role, we encourage you to apply—even if you're not sure if your background or experience is a perfect match

PySpark/SparkSQL for large-scale batch and streaming workloads