Senior Data Engineer

About Fullscript We’re an industry-leading health technology company on a mission to help people get better. We started in 2011 with one simple idea. Make it easier for practitioners to access the products they trust so they can deliver better care. That simple idea grew into a platform that powers every part of care. Today, more than 125,000 practitioners use Fullscript for clinical insights, lab interpretations, patient analytics, education, and access to high-quality supplements. Over 10 million patients rely on Fullscript to stay connected to their care plans and follow through on treatment. We build tools that make care smarter and more human. Tools that save time, simplify decisions, and help practitioners stay closely connected to the people they care for. When everything they need is in one place, they can focus on what matters most: helping people get better. This is your invitation. Bring your ideas, your grit, and your care for people. Join us and shape the future of care. The Opportunity Fullscript powers care for 125,000+ healthcare practitioners and 10M+ patients by making clinical insights, treatment workflows, and longitudinal data accessible and actionable across the platform. As the Senior Data Engineer, you’ll be part of a small team that turns messy practitioner and clinical data into reliable, analysis-ready assets that enable causal outcome modeling, lab normalization, and strategic patient and prescribing insights. This role requires healthcare-specific data experience and a practical comfort with semi-structured sources that are common in clinical environments, not just general-purpose data engineering. What you'll do Ingest and normalize heterogeneous healthcare data sources including clinical records, lab results, intake forms, and semi-structured artifacts Build robust, reproducible ELT pipelines in a cloud data stack to generate clean, longitudinal patient-level datasets Apply OCR and NLP techniques to extract structured signals from unstructured clinical documents Implement data quality frameworks, testing, version control, and CI/CD for all ingestion and transformation workflows Collaborate with data science and product partners to ensure data models support causal inference and predictive analysis needs Optimize pipeline performance and scalability in cloud data warehouses such as Snowflake or comparable technologies Produce clear documentation and operational runbooks that enable internal consumers to trust and act on healthcare datasets