Senior/Principal Platform Engineer (Clinical Analytics)
We are seeking a hybrid Platform Engineer with R/Shiny development experience to support a large-scale clinical analytics modernization initiative at a leading global pharmaceutical organization. The team has already built and deployed an enterprise R / Shiny / Posit ecosystem used across clinical development and is now focused on scaling, standardizing, and industrializing the platform across a growing portfolio of studies.
The team operates a modern enterprise R / Shiny / Posit ecosystem (Posit Workbench, Connect, Package Manager) that supports clinical data review, safety monitoring, and efficacy analysis across a large and growing portfolio of clinical studies.
This environment is transitioning from legacy, fragmented analytics systems to a standardized, scalable, and governed open-source platform, enabling reusable application frameworks, controlled software delivery, and enterprise-wide adoption of R-based clinical analytics tools.
Each clinical study is delivered as its own Shiny application; however, applications are generated from a shared, reusable framework rather than built as standalone one-off solutions, enabling scale, consistency, and regulatory alignment.
What You’ll Do
Platform Engineering (70%)
- Design, scale, and operate an enterprise Posit (RStudio) platform (Connect, Workbench, Package Manager) supporting clinical analytics workflows
- Own platform reliability, performance, and scalability including compute utilization, monitoring, and system optimization
- Build and maintain CI/CD pipelines for R and Shiny applications, including automated testing, validation, and deployment
- Define and enforce SDLC best practices, including Git workflows, version control, release management, and code quality standards
- Manage package dependencies, security vulnerabilities, and versioning strategy in a governed R ecosystem
- Support integration with broader data platforms (e.g., Databricks or equivalent large-scale compute environments)
- Contribute to modernization of development practices, including tools such as Positron and LLM-assisted development, in a controlled production setting
Shiny / Application Development (30%)
- Support delivery of production-grade Shiny applications for clinical data review (safety, AE monitoring, efficacy, topline reporting)
- Build and maintain reusable Shiny modules and components used across all study applications (tables, visualizations, workflows, review tools)
- Extend and evolve a standardized, configuration-driven (YAML-based) framework used to generate study-specific applications
- Enable controlled customization for study-specific requirements through modular framework extensions
- Optimize performance and user experience of clinical review applications
- Support troubleshooting and enhancement of deployed applications within a governed SDLC environment
What You Bring
- Bachelor’s degree in Computer Science, Engineering, Statistics, Data Science, or related field
- Strong experience with R programming and Shiny development in production environments
- Hands-on experience with Posit (RStudio) Connect / Workbench / Package Manager or equivalent enterprise R platforms
- Proven experience building or operating platform-level systems supporting multiple applications
- Strong understanding of CI/CD pipelines, Git workflows, and modern SDLC practices
- Experience working in regulated or highly governed environments (pharma, biotech, healthcare, or similar)
- Experience with Linux/Unix environments
- Strong understanding of performance optimization, scalability, and production reliability
- Experience managing dependencies, package versioning, and software security considerations
Nice to Have
- Experience with AWS or other cloud platforms
- Exposure to Databricks or large-scale distributed compute systems
- Familiarity with Python, SAS, or multi-language analytics environments
- Experience with pharmaverse tools (e.g., teal)
- Exposure to LLM-assisted development tools or Positron IDE
- Experience supporting clinical data, safety reporting, or statistical programming workflows
