About the Role
Accelerating clinical research is one of the defining challenges in healthcare. Promising therapies exist that patients can't access because the operational infrastructure to run clinical trials efficiently doesn't exist yet. We're building it. That means designing technology systems that bring order to a fragmented landscape of clinical data sources, automating the operational work that slows trials down, and turning real-world clinical data into a foundation for predictive intelligence.
We're building a uniquely valuable data asset: real-world patient and research data flowing across 80+ trial sites, spanning dozens of EHRs and clinical systems, focused on patient populations that are chronically underserved by existing clinical research infrastructure. Your job is to build the pipelines, data models, and AI infrastructure that make this asset real, from ingestion and normalization through to the systems that power predictions on top of it. You'll own data quality and observability as foundational engineering problems. You'll also have a direct hand in shaping how this data drives our AI strategy, what we model, what we predict, and what becomes possible.
This is an opportunity for someone who wants to be part of a small, fast-moving engineering team at a formative stage. You'll shape what gets built, how decisions get made, and what the team becomes.
Responsibilities
- Own the data layer and architecture: the models, schemas, and infrastructure decisions that everything downstream depends on
- Build and operate the pipelines and transformations that move data from ingestion through normalization, enrichment, and into the formats that support analytics, ML training, and production model serving
- Own data quality and observability: build the systems that make data issues visible and correctable before they compound
- Partner with ML and engineering teams to identify what's modelable, define training data requirements, and build the data foundations for new predictive capabilities
- Define how clinical and operational data is governed across the system
- Evaluate and select the tools and technologies that make up the data stack, with a clear point of view on build vs. buy
- Help shape the engineering culture of a small, growing team: how technical decisions get made, how problems get debated, what rigor looks like in practice
What We're Looking For
Required Qualifications
- 10+ years of experience in data engineering or related roles, with significant time spent building data systems
- Experience with healthcare data strongly preferred (HL7, FHIR, claims, EHR extracts) or other complex, regulated data domains
- Deep experience modeling and integrating data from multiple heterogeneous sources with inconsistent schemas and quality
- Experience applying AI and LLMs to data engineering problems: extraction, normalization, classification, entity resolution
- Strong understanding of how data infrastructure supports ML workflows from feature engineering to training data pipelines to model serving
- Fluent in SQL and at least one modern programming language (Python, Java, Scala, Go), with experience across modern data infrastructure - distributed processing, streaming, cloud-native storage, orchestration, and transformation frameworks
- Have built data systems from early stages, making foundational decisions with incomplete information
- Naturally raise the quality of the engineering around you through code review, design guidance, and honest technical conversation
Preferred Qualifications
- Experience building data infrastructure that directly supports ML model training and evaluation
- Familiarity with clinical trial operations, EDC systems, or life sciences data
- SOC 2, HIPAA or similar compliance experience baked into engineering practice
- A track record of building or improving data systems that others had given up on making reliable