You Can Read More At The Following Links

• Our Vision for AI in Pharma
• Our Current Drug Portfolio
• Our Technology & Platform

At Formation Bio, our values are the driving force behind our mission to revolutionize the pharma industry. Every team and individual at the company shares these same values, and every team and individual plays a key part in our mission to bring new treatments to patients faster and more efficiently.

About The Position As a Senior Data Engineer at Formation Bio, you will focus on building the semantic layer that makes diverse data pillars interoperable, consistent, and actionable. You’ll work across healthcare (EHR, claims, real-world data), commercial/pharma (pricing, formulary, market data), biomedical (scientific and trial data), and finance (operational and business datasets) to design models that unify disparate sources into a common language for analytics, decision-making, and AI applications.

While ingestion pipelines are part of the work, your primary responsibility will be transforming both structured and unstructured data into scalable, ontology-driven data models that teams can trust and reuse. This includes everything from traditional relational datasets to text-heavy unstructured sources that feed NLP, embeddings, and semantic search.

This role requires partnering closely with engineers, analysts, data scientists, and business stakeholders to ensure every data pillar is represented in a robust semantic foundation that supports today’s needs and tomorrow’s AI-native platforms.

Responsibilities

• Semantic Modeling & Ontologies: Build and maintain SQL/dbt models that unify datasets across healthcare, commercial/pharma, biomedical, and finance domains, leveraging ontologies (e.g., SNOMED CT, ICD, RxNorm, HL7 FHIR, OMOP).
• Structured + Unstructured Data Integration: Design models that handle not only structured datasets but also unstructured data sources (e.g., documents, free text, biomedical literature), preparing them for AI-driven applications.
• Data Layer Architecture: Own and evolve the semantic layer that transforms raw data into consistent, reusable models powering analytics and advanced AI.
• Ingestion & Integration: Contribute to pipelines that bring in data from APIs, partner feeds, flat files, and unstructured text, ensuring inputs are reliable, well-documented, and metadata-rich.
• Data Quality & FAIR Principles: Apply FAIR principles to ensure data is traceable, interoperable, and reusable across structured and unstructured domains.
• Cross-functional Collaboration: Partner with commercial, scientific, finance, and healthcare stakeholders to align semantic models with real-world use cases.
• Enablement & Documentation: Document data standards and reusable modeling patterns to empower downstream teams and reduce cognitive load.
• Future-Proofing: Anticipate how today’s semantic modeling will support tomorrow’s AI workflows such as NLP, embeddings, knowledge graphs, and retrieval-augmented generation.

About You Required Experience:

• 5+ years of experience as a Data Engineer, Analytics Engineer, or similar role in healthcare, pharma, biotech, finance, or other highly regulated industries.
• Deep expertise in at least one data domain (e.g., healthcare/EHR/claims, commercial/pharma, biomedical/scientific, or finance), with a track record of translating complex, domain-specific datasets into consistent and usable models.
• Strong SQL and data modeling skills, with proven experience designing semantic or analytical layers.
• Exposure to additional domains beyond your core area of expertise, and the ability to learn and adapt to new datasets quickly.
• Experience working with both structured data (e.g., relational tables, APIs) and unstructured data (e.g., documents, free text, biomedical literature, healthcare notes).
• Familiarity with healthcare/life sciences ontologies (SNOMED CT, ICD, RxNorm, LOINC, HL7 FHIR, OMOP, Mondo) and/or financial/commercial taxonomies.

Preferred Experience (Valued But Not Required)

• Hands-on experience with Snowflake, dbt, Dagster, and modern data stacks.
• Experience with unstructured data workflows (NLP, embeddings, semantic search, knowledge graphs).
• Understanding of regulatory and compliance considerations in healthcare, pharma, or finance.
• Practical use of metadata management and data catalog platforms.
• Hands-on experience structuring dbt projects with testing, quality checks, and reusable design patterns.

Key Attributes

• Curious & Investigative – Always looking deeper into how and why datasets work the way they do.
• Structured & Methodical – Brings rigor to semantic modeling, ontology mapping, and data quality management.
• Collaborative Partner – Works seamlessly across pillars, enabling others while owning core responsibilities.
• Adaptable – Leverages deep domain expertise while learning quickly in unfamiliar data areas.
• Enablement-Minded – Strives to reduce complexity for downstream users by standardizing and documenting.
• Future-Oriented – Builds today’s models with tomorrow’s AI-native and data-driven applications in mind.

Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with additional growth in the Research Triangle (NC) and San Francisco Bay Area. Please only apply if you reside in these locations or are willing to relocate. Compensation The target salary range for this role is: $180,000 - $230,000. Salary ranges are informed by a number of factors including geographic location. The range provided includes base salary only. In addition to base salary, we offer equity, comprehensive benefits, generous perks, hybrid flexibility, and more. If this range doesn't match your expectations, please still apply because we may have something else for you.

You will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.