Senior Data Engineer - Real World Data

About Formation Bio Formation Bio is a tech and AI driven pharma company differentiated by radically more efficient drug development. Advancements in AI and drug discovery are creating more candidate drugs than the industry can progress because of the high cost and time of clinical trials. Recognizing that this development bottleneck may ultimately limit the number of new medicines that can reach patients, Formation Bio, founded in 2016 as TrialSpark Inc., has built technology platforms, processes, and capabilities to accelerate all aspects of drug development and clinical trials. Formation Bio partners, acquires, or in-licenses drugs from pharma companies, research organizations, and biotechs to develop programs past clinical proof of concept and beyond, ultimately helping to bring new medicines to patients. The company is backed by investors across pharma and tech, including a16z, Sequoia, Sanofi, Thrive Capital, John Doerr, Spark Capital, SV Angel Growth, and others. 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 We're looking for a Senior Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help transform Real World Data (RWD)—spanning electronic health records, claims, and other longitudinal patient data sources—into structured, analytics-ready assets. In this role, you'll be partnering closely with our Data Science team not only to model and transform data, but also to actively analyze it: answering research questions, generating evidence, and supporting scientific decision-making across our drug portfolio. This position sits at the intersection of healthcare data engineering, real-world evidence analysis, and generative AI. While a strong foundation in building reliable, scalable pipelines is essential, you'll be equally expected to roll up your sleeves and work directly with the data—constructing cohorts, running analyses, and translating findings into actionable insights for scientific and business stakeholders. The ideal candidate is a hybrid of data engineer and applied scientist: someone who can build the infrastructure and then use it, with familiarity in RWD study design, GenAI fluency (e.g., LLM-based entity extraction, summarization, classification), and strong technical expertise with modern data tooling. You'll play a key role in shaping how real-world patient data becomes discoverable, structured, and impactful across the organization. Responsibilities Model and transform raw EHR and claims data into clean, canonical, and analytics-ready datasets using SQL, Python, and clinical standards like OMOP. Build and manage scalable data pipelines using Dagster for orchestration, dbt for transformation, and Snowflake as the primary compute and storage engine. Conduct hands-on RWD analyses to answer scientific and strategic research questions—including disease epidemiology, treatment patterns, patient journey characterization, and comparative effectiveness. Partner with Data Scientists and clinical leads to design and execute observational studies, translating scientific questions into well-structured, reproducible analyses. Implement data validation, completeness, and observability frameworks to ensure real-world datasets are accurate, comprehensive, and trustworthy for downstream research and product use. Apply Generative AI techniques within transformation and analysis layers to accelerate data structuring and insight generation. Communicate findings clearly to both technical and non-technical stakeholders, including summaries for portfolio teams and leadership. About You You have 5+ years of experience in data engineering, ideally with at least 2 years working in healthcare or life sciences, including direct exposure to EHR or claims datasets. You have experience with ontologies and biomedical schemas (e.g. UMLS, LOINC, ICD9/10, MeSH) and understand the modalities found within RWD — billing claims, lab results, visit notes. You're fluent in SQL and Python, and you've built and maintained production-grade pipelines that support analytics or scientific workflows. You have experience building longitudinal patient cohorts from EHR or claims data, including index date logic, washout periods, and follow-up window construction. You have a solid understanding of the causal inference frameworks such as potential outcomes and target trial emulation. You have working familiarity with real-world evidence study design concepts—such as active comparator new user designs, time-to-event outcomes, confounder adjustment, and causal discovery algorithms—sufficient to partner effectively with Data Scientists on causal inference workflows. You value clarity, documentation, and structured thinking—especially when working with complex healthcare data. You have hands-on expertise with modern data infrastructure, such as Snowflake, dbt, and Dagster. You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details. Bonus: You've worked in regulated or privacy-sensitive data environments and are familiar with governance models for PHI or sensitive data. Bonus: You have prior experience working with commercial RWD vendors (e.g. Truveta, Optum, Komodo, IQVIA) and understand the nuances of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing. Total Compensation Range: $204,500 - $267,000 Compensation Individual compensation is determined by several factors, including role scope, geographic location, and skills & experience. Your offer will reflect where you fall within the range based on these considerations. In addition to base salary, we offer equity, comprehensive benefits, and generous perks. If the posted range doesn't match your expectations, we still encourage you to apply! Where We Hire Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered. Please apply only if you reside in these locations or are willing to relocate . Equal Opportunity Formation Bio is committed to building a diverse and inclusive team. We are an equal opportunity employer and welcome candidates from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender identity or expression, sexual orientation, age, disability, genetic information, marital status, military or veteran status, or any other characteristic protected by federal, state, or local law.

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