As a Data Engineer at Regard, you'll build and maintain the data pipelines and infrastructure that turn raw clinical and product data into the metrics, insights, and capabilities that drive our product decisions, analytics, and AI research. We run an engineering-first stack that prioritizes transparent, code-driven systems over black-box services, and you'll contribute to the continued growth and reliability of our data platform.

Working closely with Product, Clinical, Engineering, and Research teams, you'll develop pipelines that bring disparate datasets together into cohesive models, support analytics initiatives, and help ensure the quality and availability of critical datasets. You'll work across the full data stack — from our AWS lakehouse to analytics, APIs, and research datasets — while growing your expertise in distributed data processing, data modeling, and platform operations.

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

Build and evolve pipelines that make clinical, application, and reference data available for product development, analytics, and research

Develop and improve data models that bring disparate datasets together into reusable, cohesive views — revealing connections across clinical workflows and product activity

Partner with Product and Clinical teams to explore datasets, develop meaningful measures of adoption and impact, and deliver insights through visualization

Optimize data processing workloads and storage patterns, using experiments and benchmarks to improve performance, scalability, and cost efficiency

Build APIs, tools, and automation that make data easier to discover and use across the organization

Contribute data workflows and datasets that support clinical AI development and evaluation in partnership with Research and Engineering

Take projects from idea through code review, testing, deployment, and production ownership — making the resulting datasets trustworthy, well-documented, and accessible under appropriate privacy controls

Minimum Qualifications

BS in Computer Science, Mathematics, Statistics, a related field, or equivalent practical experience

3+ years of experience in a data engineering role

Experience building production data pipelines and bringing multiple data sources together into useful data models

Proficiency in Python and SQL, with experience using version control, automated tests, and code review to deliver maintainable software

Experience with distributed data processing frameworks such as PySpark and cloud-based data platforms (AWS preferred)

Willingness to participate in on-call operational support for owned systems

Preferred Qualifications

Experience with Apache Iceberg or other lakehouse table formats, Dagster or a comparable orchestrator, and scalable data processing patterns

Familiarity with healthcare data, HIPAA requirements, de-identification, and standards such as FHIR, OMOP CDM, or ICD-10

Experience translating product questions into analytical datasets and visualizations, or building data services with tools such as PostgreSQL, ClickHouse, or FastAPI

Practical experience with LLM-assisted development, including reviewing generated code and independently validating results

Exposure to training and evaluation datasets, clinical NLP or LLM workflows, or tools that give AI assistants controlled access to data