Senior Data Engineer

About the role

As a Senior Data Engineer at Regard, you'll own the design, development, and production deployment of the data services that power the Regard platform. From ingesting and standardizing clinical data across health systems to making it reliably available for downstream product, analytics, and AI research workflows, you'll build and evolve the infrastructure that enables the platform. You'll help set the technical direction for the data team — designing lakehouse architecture, leading experimentation with processing and storage approaches, and mentoring engineers — while remaining hands-on with implementation and production delivery. We prioritize transparent, code-driven systems over black-box services, and you'll help architect the data platform that supports that philosophy.

About Regard

Regard's mission is to bring world-class healthcare to everyone. Our technology reasons through a patient's entire medical record to recommend diagnoses that would otherwise be missed, in real time at the point of care, during chart review, and in population-wide screening to identify patients who qualify for lifesaving treatment.

We work alongside some of the top health systems in the country to lead the change this industry needs. We're excited by challenges, mission-oriented work, and meaningful relationships. We want you to join us.

Our Tech Stack:

- Lakehouse & processing: Python, SQL, Amazon S3 and S3 Tables, Apache Iceberg, PySpark, EMR Serverless, AWS Glue, Athena

- Orchestration & infrastructure: Dagster, Kubernetes, Pulumi, GitLab CI/CD

- Data services & analytics: PostgreSQL, ClickHouse, FastAPI, Metabase

Responsibilities:

- Design and evolve the lakehouse architecture and shared processing capabilities that let the team build with an expanding range of data sources and use cases

- Connect disparate clinical and product datasets into cohesive, reusable models that deepen understanding of clinician workflows and unlock new product and research opportunities

- Lead continuous experimentation with Spark workloads, Iceberg storage, and orchestration — testing optimizations and measuring their effects on performance, scalability, and cost

- Partner with Product and Clinical teams to identify valuable questions, define meaningful measures of adoption and impact, and deliver insights through visualization

- Build data APIs, developer tooling, and self-service capabilities that make it easier for colleagues and AI assistants to discover data and create new applications

- Partner with Research and Engineering on clinical AI workflows, including dataset curation, de-identification, human review, and reproducible model evaluation

- Own projects from technical design through deployment and production operation, setting standards for data quality, documentation, and privacy

- Guide engineers through design and code review, develop reusable platform capabilities, and create space for the team to experiment and learn