Senior Data Engineer

Formation Bio•Boston, NY

3d•Hybrid

About The Position

As a Senior Data Engineer at Formation Bio, you will build the trusted data systems that support clinical operations, drug asset evaluation, business development, analytics, and machine learning through AI Enabled Employees and Agents. You will work across clinical, operational, and third-party data sources to design and operate reliable ingestion pipelines, transformations, data models, and data products. This role sits at the intersection of Product Engineering, Data Engineering, and Data Science. You will help shape how application data is modeled and exposed, own shared data models and production data products, and prioritize data platform work against clinical and business needs. You will work closely with Product Engineering on application data models and contracts, with Data Science on training datasets and ML use cases, and with human and AI consumers of the data platform. A data product is not complete merely because it is technically correct or available in a warehouse. It should be understandable to people, usable by applications, useful to Data Science, and structured so AI Enabled Employees and Agents can access it reliably and safely.

Requirements

5+ years of relevant data engineering experience building and operating production data systems.
Experience with pharmaceutical, biology, HIPPA or other regulated data core to BioTech is required.
Strong Python and SQL skills, with deep experience in data modeling and warehouse systems, especially Snowflake.
Experience with Dagster as an orchestration systems (or equivalent) transformation tooling such as dbt or an equivalent approach.
Experience with data contracts, schema evolution, data quality testing, observability, lineage, and production incident response.
Experience integrating messy clinical, operational, vendor, or otherwise complex source data.
Working knowledge of Docker, GitHub, and Terraform or OpenTofu sufficient to partner effectively with SRE.
Experience building data products and access patterns for applications, Data Science, analytics, human users, and AI Enabled Employees and Agents.
Strong judgment about when to build reusable platform capabilities versus one-off stakeholder solutions.
Daily fluency with AI tools and the ability to validate generated code, transformations, and data-modeling decisions.
Exceptional collaboration and communication skills across Product Engineering, Data Science, Clinical Operations, Data Management, Business Development, and other non-technical partners.

Nice To Haves

Experience working within and building validated computerized systems (CSV) is a plus.

Responsibilities

Design and operate production data systems that ingest clinical, operational, and third-party vendor data into reliable, queryable data products.
Own shared and canonical data models, data contracts, transformations, orchestration, warehouse models, and downstream interfaces.
Partner with Product Engineering on application data models, source-system contracts, APIs, events, and data access patterns.
Partner with Data Science on productionized training datasets, feature pipelines, data interfaces, and ML use cases.
Turn recurring data cleaning, normalization, and transformation work into versioned, tested, observable, and maintainable production pipelines.
Build data products for clinical operations, asset evaluation, Business Development, analytics, machine learning, and AI Enabled Employees and Agents.
Design data products that are semantically clear, discoverable, machine-readable, permission-aware, traceable, and safe to query.
Establish strong data quality, testing, freshness, completeness, lineage, documentation, and observability practices.
Own data governance practices for sensitive and regulated data, including access controls, auditability, traceability, and appropriate data handling.
Participate in support and incident response for data platform issues, including diagnosis, stakeholder communication, remediation, and prevention of recurrence.
Use AI tools, including LLMs and agentic coding systems, to accelerate pipeline development, data modeling, debugging, documentation, and data quality investigation while validating their output.
Contribute to architecture and design reviews, mentor other engineers, and improve the engineering practices used across the organization.