Solution Architect (projekt VIDRU)

We are delivering a multi-year programme to transform fragmented legacy discovery data into a harmonised, AI-ready and FAIR-enabled data foundation supporting seven scientific data workflows across vaccines and infectious disease research.

• Proven experience as a Solution Architect delivering technology solutions in pharma, biotech, or life sciences R&D environments.
• Strong understanding of scientific/discovery data workflows and ability to engage directly with scientists and research teams.
• Proven experience designing data mesh and/or data product architectures.
• Deep, hands-on experience with Microsoft Azure and Databricks, including lakehouse and Delta architectures.
• Experience integrating ELN, LIMS, laboratory systems, sample management/inventory solutions, and scientific instruments.
• Strong knowledge of metadata management, semantic modelling, data lineage, and data standards.
• Practical experience applying FAIR principles and data governance.
• Experience working with structured and unstructured scientific data, including large-volume instrument data.
• Understanding of AI/ML data enablement and characteristics making data suitable for machine learning and AI applications.
• Experience in regulated life sciences environments with knowledge of distinctions between GxP and non-GxP research.
• Strong stakeholder management skills and ability to influence across a matrix without direct authority.
• Ability to move between scientific, business, and technical discussions and translate complex requirements into practical architecture.
• Strong English communication skills.

• Define and maintain the end-to-end target architecture across scientific data workflows.
• Design data mesh and data product architectures, including ownership, data contracts, quality expectations, and lifecycle management.
• Design cloud data solutions using Microsoft Azure and Databricks, including lakehouse/Delta architectures, data pipelines, secure networking, secrets management, and observability.
• Define integration architectures connecting ELN, LIMS, sample inventory systems, and scientific instruments with governed data platforms.
• Architect solutions for both structured and unstructured scientific data, including high-volume instrument outputs, provenance, and schema evolution.
• Define metadata models, naming conventions, identifier strategies, and data lineage.
• Operationalise FAIR data principles supporting cataloguing, discoverability, stewardship, and appropriate access models.
• Ensure scientific data is structured and exposed to enable AI/ML use cases, including training and feature pipelines and machine-accessible interfaces.
• Define and promote architectural standards while maintaining flexibility for exploratory research environments.
• Apply appropriate data integrity and regulatory controls across GxP and non-GxP R&D environments.
• Work closely with scientists and research teams to understand assay and experimental workflows and translate them into technical solutions.
• Align scientific leads, business SMEs, and multiple technology functions in a matrix environment.
• Review solution designs and implementation quality and provide architectural guidance throughout delivery.