Senior Data Engineer - Real World Data

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.

Requirements

• 5+ years of experience in data engineering, with at least 2 years working in healthcare or life sciences, including direct exposure to EHR or claims datasets.
• Experience with ontologies and biomedical schemas (e.g. UMLS, LOINC, ICD9/10, MeSH) and understanding of modalities found within RWD — billing claims, lab results, visit notes.
• Fluency in SQL and Python; experience building and maintaining production-grade pipelines that support analytics or scientific workflows.
• Experience building longitudinal patient cohorts from EHR or claims data, including index date logic, washout periods, and follow-up window construction.
• Solid understanding of causal inference frameworks such as potential outcomes and target trial emulation.
• 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.
• Hands-on expertise with modern data infrastructure, such as Snowflake, dbt, and Dagster.
• Value clarity, documentation, and structured thinking—especially when working with complex healthcare data.

Nice-to-Haves

• Experience in regulated or privacy-sensitive data environments and familiarity with governance models for PHI or sensitive data.
• Prior experience working with commercial RWD vendors (e.g. Truveta, Optum, Komodo, IQVIA) and understanding the nuances of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing.