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# Senior Data Engineer (5+ years)

- **Company:** Foresite Labs, LLC
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $185,000.0 - $221,400.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Unix, Information Engineering, Extract Transform Load (ETL), Data Transformation, Github, Identity and Access Management, Python (Programming Language), PostgreSQL, SQL Databases, Parquet, Pulumi, Business Intelligence Development Studio, Large Language Models, Apache Spark, Generative AI, Streamlit Framework, Terraform, Data Pipelines, Serverless Computing, Docker
- **Published:** May 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1bd97d667dce4191

## About the Role

Do you have experience in UNIX?, * 5+ years of professional data engineering experience designing, building, and operating production pipelines end-to-end - including schema design for analytical workloads, entity resolution across messy real-world sources, and data quality enforcement at the pipeline level.

 * Deep fluency in Python and SQL, writing performant, well-tested data transformation code (dbt or similar), with production experience in pipeline orchestration (e.g., Airflow, Prefect) covering DAG design, scheduling, dependency management, retry/backfill patterns, and alerting.
 * Hands-on cloud data stack experience across AWS or GCP (e.g., managed Postgres, object storage, query engines, serverless ETL) and working knowledge of IAM, networking, and infrastructure patterns; comfortable with Spark or Trino at scale over Parquet/Iceberg. Terraform, CDK, or Pulumi experience is a plus.
 * Exposure to vector databases (Pinecone, pgvector, Weaviate, or similar) with an understanding of how embedding-based retrieval fits into LLM-powered applications.
 * Comfortable building and navigating Unix environments, containers (Docker), and CI/CD pipelines (GitHub Actions or similar).
 * Mindset for rapid and early-stage execution: bias for action, ownership of ambiguous problem spaces, and demonstrated ability to prioritize and wear multiple hats.
 * Strong written and verbal communication skills - comfortable explaining complex ideas clearly to technical and non-technical partners., * Familiarity with biomedical or life sciences data (e.g., clinical trials, genomics, drug discovery, pharma commercial data).
 * Familiarity with LLM application patterns: prompting, context engineering, tool use, structured outputs, retrieval-augmented generation (RAG).
 * Experience with dashboard/BI tooling (Streamlit, Retool, Metabase, or similar).



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