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Role Summary
Discovery, Preclinical, and Translational Solutions (DP&TS) sits at the center of Pfizer’s scientific software work, building the digital tools that help researchers move from target identification through clinical translation.
This is a hands-on engineering role. You’ll build and run workloads on AWS and GCP using Kubernetes and containers, and you’ll write production code in Python or JavaScript/TypeScript. You’ll own both the platforms and the applications that run on them.
We’re looking for a Solution Engineer to own data visualization, analytics, and collaboration capabilities for integrative biology within DP&TS. In this individual contributor role you’ll design, build, and maintain the platform, not just direct others. You’ll also work closely with the aligned capability lead to prioritize what gets built.
You’ll need strong technical depth in data visualization, analytics, collaboration platforms, and cloud-native engineering, along with the ability to set standards for the capability area. You’ll lead contractors as needed and partner across scientific, product, and engineering stakeholders to deliver secure, scalable solutions.
Reporting to the Principal Engineer, Integrative Biology Solutions, you’ll own the technical direction for visualization, analytics, and collaboration capabilities. This role doesn’t conduct data science research but you’ll need a strong grasp of how data scientists work and what they need from visualization and collaboration tooling to build the right platform.
Your technical decisions affect the speed, quality, and reproducibility of research across DP&TS. The problems cross scientific workflows, data engineering, and cloud infrastructure.
Key Responsibilities
* Own and Lead Solution Implementation: own visualization, analytics, and collaboration solutions for Integrative Biology end-to-end: design, build, deployment, and maintenance.
* Collaborate with Capability Lead on Prioritization and Roadmap: work with the R&D Capability Lead to prioritize and scope features; translate scientific needs into concrete technical requirements and delivery plans.
* Design and Implement Advanced Visualization and Analytics Solutions: architect, build, and maintain scalable visualization and analytics solutions for Integrative Biology research, including interactive dashboards, visual exploration tools, and platforms that handle large-scale biological datasets.
* Build and Operate Cloud Infrastructure: design, provision, and operate infrastructure on AWS and GCP using infrastructure-as-code. Deploy and manage containerized workloads on Kubernetes (EKS, GKE), including cluster configuration, networking, access control, scaling, and cost management.
* Develop Production Software: write, test, and maintain production-quality code in Python or JavaScript/TypeScript, including services, APIs, automation, and web-based visualization applications. Follow sound engineering practices: version control, code review, automated testing, and CI/CD.
* Deliver Collaboration Solutions: build and maintain collaboration environments that let data scientists and research teams share and explore biological data securely and reproducibly.
* Lead Contractor Teams and Delivery: direct contractor teams when needed: provide technical guidance, review work, and hold delivery to quality and security standards.
* Define Standards and Best Practices: set and maintain technical standards and governance frameworks for the capability area, ensuring consistency and quality across the Integrative Biology ecosystem.
* Drive Continuous Improvement and Operational Excellence: proactively identify and address reliability, performance, scalability, and security gaps through monitoring, logging, and alerting across cloud and Kubernetes environments. Keep solutions current as scientific and operational needs evolve.
* Understand and Translate Scientific Needs: build a working knowledge of how data scientists use visualization, analytics, and collaboration tools — and use that understanding to build the right platform, not just a technically sound one.
Basic Qualifications
Education: Bachelor’s degree in a relevant field (e.g., Computer Science, Data Science, Bioinformatics, Engineering, or related discipline)
Experience:
* 6+ years of progressive experience in solution engineering, software engineering, or infrastructure engineering, with hands-on design and implementation of cloud platforms and data visualization, analytics, or collaboration solutions
* Proven experience owning and leading the end-to-end delivery of technical solutions in a complex enterprise or scientific environment, including requirements gathering, solution design, implementation, and ongoing maintenance
* Experience leading contractor teams or matrixed delivery teams to implement technical solutions, with strong ability to provide technical direction, manage work quality, and drive delivery outcomes
Technical Skills
* Proficiency in Python and/or other modern high-level languages used in data engineering and scientific computing
* Hands-on experience building and operating workloads on both AWS and GCP
* Production experience with Kubernetes (EKS, GKE, or similar) and containerization (Docker), including Helm or equivalent packaging
* Strong software development skills in Python or JavaScript/TypeScript, with experience shipping and maintaining production code
* Experience with infrastructure-as-code (Terraform preferred) and CI/CD pipelines (e.g., GitHub Actions)
* Working knowledge of data engineering and data platform concepts, including data ingestion, transformation, and storage patterns relevant to supporting visualization and analytics workloads in cloud environments
Other Requirements
* Demonstrated experience in working with regulated data, compliance frameworks, and secure development practices
* Ability to lead complex engineering efforts across global, cross-functional teams
* Fluent in English; capable of clear technical communication across scientific and engineering disciplines
Preferred Qualifications
Education: Master’s or PhD in a relevant field (e.g., Computer Science, Data Science, Bioinformatics, Engineering, or related discipline)
Domain Expertise
* Experience working in pharmaceutical R&D, life sciences, academic research, or a similar scientific research environment; familiarity with how data scientists and computational biologists work and what visualization, analytics, and collaboration tools they depend on
* Conceptual understanding of the data and analytical workflows used in integrative biology research, including the types of visualizations, analysis outputs, and collaborative review processes involved in omics, imaging, proteomics, or other high-dimensional biological data domains
Cloud and Platform Engineering
* AWS or GCP professional certifications; CKA or CKAD
* Experience with GitOps tooling (e.g., Argo CD, Flux)
* Experience with observability tooling (e.g., Prometheus, Grafana, CloudWatch, Cloud Monitoring)
* Experience building web front ends with modern frameworks (e.g., React) for data visualization
Research Domain Awareness
* Hands-on experience with data visualization and analytics tools and platforms used in omics, imaging, proteomics, or other high-dimensional biological data analysis (e.g., Tableau, Spotfire, custom D3/Plotly/Dash applications)
* Experience implementing or administering scientific collaboration solutions (e.g., electronic lab notebooks, knowledge management platforms, shared analytical workspaces) in a research or enterprise environment
Scientific Collaboration
* Demonstrated ability to work collaboratively with data scientists, computational biologists, or research scientists to understand their workflows, translate their needs into technical requirements, and deliver solutions tha