Lab Informatics Engineer

In this role you design and deploy scalable lab informatics solutions to accelerate R&D workflows at Lonza. You’ll connect science with data engineering to improve experiment traceability and data quality while supporting cross-functional teams. You’ll shape the digital lab ecosystem and drive adoption through training and documentation. This is a hands-on role in a cutting-edge, collaborative R&D environment with a clear impact on how research data is captured, stored, and analyzed.

• agile career and dynamic working culture
• inclusive and ethical workplace
• compensation programs recognizing high performance
• role- and location-based benefits
• global benefits package available

• Lead implementation, configuration, and optimization of lab informatics systems (ELN, LIMS, SDMS) to enhance scientific workflows
• Collaborate with R&D scientists to translate experimental processes into scalable digital solutions
• Design data models, metadata structures, and workflows aligned with FAIR data principles
• Build and maintain integrations between lab platforms, analytics tools, automation systems, and cloud environments using APIs and middleware
• Develop data flows to improve interoperability, reduce manual effort, and ensure high-quality experimental data capture
• Contribute to digital architecture discussions and evolution of Lonza’s R&D data ecosystem
• Deliver user training, documentation, and ongoing support to drive adoption and continuous improvement

• MSc (or equivalent) in a related scientific field
• Experience in pharma/biotech or life sciences R&D environments
• Hands-on experience implementing or optimizing lab informatics systems (ELN, LIMS, SDMS)
• Strong understanding of scientific workflows and experimental data lifecycle management
• Experience with data modelling concepts and metadata standards; familiarity with FAIR data principles desirable
• Programming or scripting experience (Python, R, SQL) and API integration (REST, GraphQL)
• Strong communication skills and ability to collaborate across scientific and technical teams
• Experience with cloud platforms (Azure, AWS, GCP), lab automation, or bioinformatics data pipelines a plus
• Willingness to apply transferable technical skills in a scientific R&D setting

• Cross-functional collaboration
• Effective communication
• Problem-solving orientation
• ELN
• LIMS
• SDMS