Solution Architect (projekt VIDRU)

Solution Architect – Scientific Data Platform About the project 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 . The programme is delivered through multiple parallel value streams supported by a shared technical capability stream. We are looking for a Senior Solution Architect to own the end-to-end architecture across these workflows and help shape how scientific data is captured, integrated, governed and made available for downstream analytics and AI. The role As a Solution Architect , you will define the target-state architecture, integration patterns and data standards across the programme. You will work directly with scientists, business SMEs and technology teams to translate real-world research workflows into scalable technical solutions. This is a hands-on architecture role . You will not be limited to producing governance documentation — you will design solutions, review implementation quality and drive architectural decisions throughout delivery. What makes this role interesting Opportunity to shape the architecture of a major scientific data transformation programme from the ground up. Direct impact on how discovery data is made available for AI, analytics and future research . Exposure to cutting-edge Azure, Databricks, data mesh, FAIR and AI/ML capabilities. Close collaboration with scientists and technology teams across a highly specialised R&D environment. A genuine architecture + delivery role rather than a governance-only position. Daily tasks: - 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 in a way that enables AI/ML use cases, including training and feature pipelines and machine-accessible interfaces. - Define and promote architectural standards across the programme while maintaining appropriate 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. What we're looking for 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 the 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 what makes data suitable for machine learning and downstream AI applications. Experience working in regulated life sciences environments and understanding the distinction between GxP and non-GxP research . Strong stakeholder management skills and the ability to influence across a matrix without direct authority. Ability to move between scientific, business and technical discussions and turn complex requirements into practical architecture. Strong English communication skills. Must have: Microsoft Azure, AI, Machine learning, GXP, Stakeholder management