Senior Data Engineer

Foresite Labs is a translational R&D team that derives insights from precision measurement and population-scale biology and genetics to address unmet clinical needs. We use human genetics to systematically dissect and understand human disease biology and develop and critically evaluate therapeutic hypotheses. We engage in translational research, transforming basic insights into therapeutic opportunities. Our work supports drug discovery and company formation, and provides the core around which new ideas are realized and incubated.

We offer competitive salaries, excellent benefits, a flexible work environment, and the opportunity to learn from top thinkers in various disciplines. Foresite Labs is headquartered in San Francisco and Boston.

• Build and own production data infrastructure. Design, implement, and operate deterministic data pipelines that feed intelligence layers; ingest clinical, financial, scientific, and commercial data from REST APIs, XML feeds, and file-based sources into clean, queryable analytical layers; own the full lifecycle: pagination, rate limiting, auth, schema drift, idempotency, retries, monitoring, and alerting.
• Model and curate high-quality data assets. Perform entity resolution, schema design, and quality enforcement across disparate and heterogeneous data sources; ensure downstream models, agents and dashboards operate on clean and trustworthy data.
• Support AI-native workflows. Build vector infrastructure (e.g., embeddings, indexing, retrieval) and structured data interfaces that GenAI agents and LLM orchestrators depend on; ensure AI layers have the right data in the right shape at the right time.
• Uphold high engineering standards and collaborate broadly. Lead code and design reviews, establish testing and observability best practices, and mentor peers; partner with ML engineers, computational biologists, and company founders to translate scientific and business goals into maintainable, and scalable technical solutions.
• Leverage agentic coding tools (Claude Code, Codex, or similar) to accelerate prototyping, refactoring, and debugging.

• 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 (Git Hub 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:…