Senior Data Engineer

Senior Data Engineer

We are looking for a Senior Data Engineer to work with a leading generative AI company in healthcare.

Location: Palo Alto, CA office. This role is expected to be in our Palo Alto office five days a week. Benefits include 401k, equity, and on-site lunch.

Why Join Our Team

Reinvent healthcare with AI that puts safety first.

Work with the people shaping the future.

Backed by the world’s leading healthcare and AI investors.

Build alongside the best in healthcare and AI.

What You'll Do

Build and operate data platforms and pipelines (batch/stream) that feed training, RAG, evaluation, and analytics using tools like Prefect, dbt, Airflow, Spark, and cloud data warehouses (Snowflake/BigQuery/Redshift).

Own data governance and access control: implement HIPAA‑grade permissioning, lineage, audit logging, DLP, manage IAM, roles, and policy‑as‑code.

Ensure reliability, observability, and cost efficiency across storage (S3/GCS), warehouses, ETL/ELT, SLAs/SLOs, data quality checks, monitoring, and disaster recovery.

Enable self‑service analytics via curated models and semantic layers; mentor engineers on best practices in schema design, SQL performance, and data lifecycle. Partner with ML/Research to provision high‑quality datasets, feature stores, and labeling/eval corpora with reproducibility (versioning, metadata, data contracts).

What You Bring

Must‑Have Qualifications

5+ years of software or data engineering experience, with 3+ years building data infrastructure, ETL/ELT pipelines, or distributed data systems.

Deep experience with Python and at least one cloud data platform (Snowflake, Databricks, BigQuery, Redshift, or equivalent).

Familiarity with orchestration tools (Airflow, Prefect, dbt) and infrastructure‑as‑code (Terraform, CloudFormation).

Strong understanding of data security, access control, and compliance frameworks (HIPAA, SOC 2, GDPR, or similar).

Proficiency with SQL and experience optimizing query performance and storage design.

Excellent problem‑solving and collaboration skills — able to work across engineering, ML, and clinical teams.

Comfortable navigating trade‑offs between performance, cost, and maintainability in complex systems.

Nice‑to‑Have

Experience supporting ML pipelines, feature stores, or model training datasets.

Familiarity with real‑time streaming systems (Kafka, Kinesis) or large‑scale unstructured data storage (S3, GCS).

Background in data reliability engineering, data quality monitoring, or governance automation.

Experience in healthcare, safety‑critical systems, or regulated environments.

If you’re passionate about building data systems that power safe, real‑world AI, we’d love to hear from you. Apply today and take the next step in your career!

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