Data Engineer

Solid engineering fundamentals and experience building pipelines from scratch Python and SQL fluency; comfortable across relational + NoSQL systems Experience with orchestrators like Temporal, Airflow, or Dagster Hands-on with BigQuery, BigTable, and core GCP data tooling Ability to turn messy, ambiguous data problems into clear, scalable solutions Startup or small-team experience; comfortable moving fast with ownership Communication skills, attention to detail, and a bias toward clarity and reliability Experience with HIPAA/PHI or regulated healthcare data (nice to have) Background with time-series data or event-driven architectures (nice to have) Familiarity with dbt or similar transformation frameworks (nice to have) Experience with healthcare, diagnostics, or ML/AI workloads (nice to have)

Build and run pipelines that turn raw, messy healthcare data into clean, trusted, usable information Power customer products, internal analytics, and AI models Design how data flows through an entire diagnostics ecosystem Build scalable, cloud-native pipelines on GCP Hunt down edge cases, build guardrails for quality, and ship systems other engineers rely on daily Designing and operating ingestion, transformation, and replication pipelines on GCP Managing orchestration and streamlining ELT/ETL workflows (e.g., Temporal) Creating clean, scalable, analytics-ready schemas in BigQuery Implementing monitoring, alerting, testing, and observability across data flows Integrating data from APIs, operational databases, and unstructured sources Collaborating with product, engineering, analytics, and compliance on secure, high-quality data delivery