Data Scientist

About DelRicht Research

DelRicht Research is a national network of integrated clinical research sites embedded within physicians’ practices that offer the benefit of a dedicated research team with the support of a centralized team.

Our clinical trials provide new treatment options for our patients, and ultimately our goal is to move medicine forward by getting new medications FDA approved. Presently our network stretches across 33 clinical research sites throughout the United States in 18 states.

As DelRicht scales, technology and data are becoming central to how we operate. We are building a modern, integrated data and AI platform — and this role is a core part of that foundation.

The Role

This is a high-ownership engineering role for someone who thinks in systems. You will be responsible for designing, building, and maintaining DelRicht's data platform, cloud infrastructure, integration architecture, and AI-powered applications — working across every layer from source systems to operational tools.

Our environment is built on Google Cloud Platform and the broader Google ecosystem. The ideal candidate is deeply comfortable in that stack and brings hands-on experience building AI-augmented workflows using tools such as Vertex AI, Gemini API, Document AI, and LLM-based tooling including Claude and Claude Code.

You will work directly with our existing Data Scientists and business stakeholders to turn operational problems into reliable, scalable, automated solutions.

What You'll Own

Data Platform & Architecture

- Maintain and evolve DelRicht's centralized BigQuery data warehouse — schemas, datasets, data models, and access controls

- Build and maintain ETL/ELT pipelines connecting clinical, operational, financial, and CRM source systems

- Design and implement dev, staging, and production environment structure across the data platform

- Establish data governance, documentation, and pipeline monitoring standards

- Optimize BigQuery performance, query structure, and cost management

- Partner with analysts to build trusted, reusable data models rather than one-off datasets

Cloud Infrastructure & DevOps

- Design and maintain GCP infrastructure across Cloud Run, Cloud Functions, Cloud Storage, Cloud SQL, Pub/Sub, and IAM

- Build and own CI/CD pipelines for data and application deployments

- Establish infrastructure-as-code practices and environment parity (dev → staging → production)

- Manage service account security, least-privilege access, and GCP IAM governance

- Monitor pipelines, set alerting, and maintain platform reliability

AI & Automation

- Build and maintain AI-powered pipelines and applications using Vertex AI, Gemini API, Document AI, and related Google AI services

- Work fluently with LLM-based development tools including Claude and Claude Code as part of day-to-day engineering workflows

- Develop automation that replaces manual, spreadsheet-driven processes with scalable, reliable systems

- Evaluate and integrate emerging AI tooling where it creates genuine operational leverage

Integrations & APIs

- Build and maintain integrations across operational systems including CRIO, Salesforce, Greenhouse, and financial platforms

- Design scalable API integrations using REST, webhooks, and managed platforms such as Fivetran

- Own integration reliability, monitoring, and failure handling

- Maintain and improve MuleSoft or successor integration middleware where applicable

Google Workspace & Apps Script

- Build automation and lightweight applications integrated with Google Sheets, Drive, Gmail, and Workspace APIs

- Migrate manual Sheets-based workflows to purpose-built, maintainable solutions

- Develop advanced Apps Script solutions as operational bridges while longer-term platform solutions are built

Application Development

- Build internal tools and lightweight applications that support operational teams

- Write clean Python and/or Node.js backend code for services, APIs, and automation

- Develop simple front-end interfaces where needed — comfort with React or equivalent is a plus, but this is not primarily a frontend role

What We're Looking For

Required

- 4+ years of professional experience in data engineering, platform engineering, or a closely related technical discipline

- Strong, hands-on BigQuery experience — data modeling, SQL optimization, schema design

- Fluency in Python for data pipelines, API integrations, and automation

- Experience building and maintaining ETL/ELT pipelines in a cloud environment

- Hands-on GCP experience across Cloud Run, Cloud Functions, Cloud Storage, and IAM

- Experience building and maintaining REST API integrations and webhooks

- Strong understanding of environment management (dev/staging/production), CI/CD pipelines, and deployment practices

- Experience with service account security and cloud IAM governance

- Strong SQL skills and ability to write complex analytical queries

- Ability to take technical projects from business requirements through production deployment independently

- Strong communication skills — able to work directly with non-technical stakeholders

Strongly Preferred

- Hands-on experience with Vertex AI, Gemini API, Document AI, or comparable AI/ML cloud services

- Experience working with LLM-based tooling (Claude, Claude Code, OpenAI, or similar) in a production or near-production context

- Experience with Fivetran or comparable managed integration platforms

- Salesforce integration experience — APIs, data model understanding, or middleware

- Looker or Looker Studio dashboard and data model development

- Google Apps Script development

- Infrastructure-as-code experience (Terraform or Cloud Deployment Manager)

- Experience in healthcare, clinical research, or another regulated industry

Nice to Have

- Familiarity with HIPAA and clinical data privacy requirements

- Experience with data orchestration tools such as Airflow or comparable

- Docker and containerization experience

- Frontend development experience (React, TypeScript)

- Experience with n8n or similar workflow automation platforms

What Success Looks Like

In your first six months, you will have a clear understanding of DelRicht's full data and integration architecture, have contributed meaningfully to pipeline reliability and environment structure, and have shipped at least one automation or integration that materially reduces manual work for an operational team.

Over the first year, you will be a trusted technical owner of DelRicht's data platform — capable of taking projects independently from requirements to production, building AI-augmented workflows that scale with the business, and helping establish the engineering standards that support DelRicht's next stage of growth.

Why DelRicht Research

This is a rare opportunity to have genuine ownership across an organization's entire data and technology infrastructure at a moment when that infrastructure is actively being built. You will work on real operational problems, ship tools that teams use every day, and help define the technical architecture that carries the company forward.

We are building a modern, AI-native data platform in a domain — clinical research — where the work directly impacts patient outcomes and the advancement of medicine. If you enjoy building, owning outcomes, and working at the intersection of data, AI, and real operational complexity, this is the right environment.