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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

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