Lead Data Architect – Clinical Imaging and AI Platforms
This role is responsible for defining the enterprise data architecture strategy and establishing standards for metadata, interoperability, lineage, and AI-ready data models. It will enable scalable analytics and AI/ML workflows for Research (REDs) and Product Development (PD) while driving modernization of imaging data platforms in partnership with engineering, informatics, and governance teams.
The Lead Data Architect serves as the strategic bridge between scientific, business, and technology organizations across Research, Product Development, RIS, and RDT, translating business priorities into scalable, secure, and interoperable data architectures. This role ensures alignment with GxP, SaMD, FAIR, and clinical data governance frameworks.
Define and execute the enterprise data architecture strategy and roadmap for multi-modal imaging and research data within the Global Imaging Platform (GIP)
Design scalable and interoperable data architectures supporting Radiology, Digital Pathology, AI/ML, clinical, and translational research workflows
Establish and enforce standards for data modeling, metadata management, lineage, interoperability, cataloging, and governance across structured and unstructured datasets in close partnership with the Data managers and Study teams
Lead the design and implementation of cloud-native data platforms, data lakes/lakehouses, and distributed analytics ecosystems supporting general R&D; and clinical initiatives
Partner with scientific, clinical, and technical stakeholders to translate business and research requirements into scalable, compliant, and reusable data solutions
Act as the primary architecture lead for imaging and multi-modal data ecosystems, coordinating across gRED, pRED, PD, RDT, RIS, and enterprise IT functions
Ensure alignment with GxP, FAIR standards, HIPAA/GDPR, security, privacy, and enterprise governance standards across all data architecture initiatives




Drive enterprise-wide adoption of common data models, interoperability standards, APIs, and semantic frameworks to improve data accessibility and reusability
Collaborate with engineering and platform teams to design robust data ingestion, transformation, storage, and access patterns optimized for AI/ML and advanced analytics, focussed on biomarker research and analysis of Imaging data
Define architecture patterns for high-volume imaging and pathology data, including metadata indexing, streaming, archival, and federated access strategies
Lead modernization efforts for legacy data environments towards scalable and efficient hybrid architectures
Drive automation and standardization across data lifecycle management, data quality monitoring, observability, and governance workflows
Identify and implement opportunities to improve scalability, cost optimization, performance, automation, and operational efficiency across enterprise data platforms
Monitor emerging technologies and industry trends in data platforms, imaging data formats, AI/ML ecosystems, digital pathology, interoperability, and data standards to guide innovation and future-state architecture
Drive the evolution of AI-ready and semantically enriched data architectures that support advanced analytics, machine learning, and emerging generative AI use cases
Foster a culture of continuous improvement, technical excellence, collaboration, and knowledge sharing within Roche’s global data and imaging community
Contribute to enterprise architecture review boards and governance councils to ensure alignment with long-term platform and data strategy




Demonstrated experience defining technical direction, coaching junior architects and engineers, and successfully driving medium to large-scale enterprise data initiatives across complex organizations
Proven track record contributing to enterprise architecture strategy for major data platforms, products, or services within regulated or highly governed environments
Deep expertise in enterprise data architecture, distributed data systems, cloud-native platforms, and scalable analytics ecosystems
Strong experience designing modern data platforms using technologies such as Snowflake, Databricks, AWS Datastores, Azure, GCP, or equivalent cloud ecosystems
Expertise in data modeling methodologies, metadata management, master/reference data management, data lineage, and enterprise data governance frameworks
Experience architecting AI/ML-ready data environments supporting advanced analytics, model training, reproducibility, and operational deployment
Proficiency designing scalable ingestion and processing architectures for large-scale structured and unstructured datasets, including imaging and digital pathology data
Experience implementing reusable Infrastructure as Code (IaC) frameworks, CI/CD pipelines, and automated deployment patterns for cloud data platforms
Ability to design observability, monitoring, data quality, and operational resilience frameworks across interconnected enterprise systems
Strong background in security architecture, privacy controls, access management, and regulatory compliance in healthcare or life sciences environments
Strong coaching and mentorship skills, including experience mentoring senior engineers, other architects, and platform teams while promoting architectural consistency and engineering quality
Practical experience working with regulatory, quality, and compliance teams to align enterprise data practices with Roche QMS and governance requirements

📌 Data Architect / BI Architect (Madrid)
🏢 Roche
📍 Madrid