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Die Position
The Lead Data Architect –Clinical Imaging and AI Platforms will lead the design, governance, and evolution of enterprise imaging and multi-modal data ecosystems, ensuring alignment with GxP, SaMD, FAIR, and clinical data governance frameworks. This role 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.
The role is responsible for defining 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.
Job Responsibilities
- Define and execute the enterprise data architecture strategy and roadmap for multi-modal imaging and research data within theGlobal 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 global R 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 withGxP, 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.
- Oversee architecture reviews, technical governance, and cross-functional design decisions to ensure consistency, scalability, and operational excellence.
- 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.
- Promote AI-enabled automation and metadata-driven architectures to accelerate analytics, reproducibility, compliance, and operational efficiency.
- 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.
Qualifications:
- Preferably has 8 - 10 years of industry experience in the relevant fields and supporting education in Technology.
- 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 asSnowflake, 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.
- Strong understanding of healthcare and life sciences interoperability standards such asDICOM, HL7/FHIR, OMOP, CDISC, SDTMor related 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.
- Strong communication and stakeholder management skills, capable of translating complex technical concepts into business value and building partnerships across scientific, engineering, clinical, and executive stakeholders.
- Practical experience working with regulatory, quality, and compliance teams to align enterprise data practices with Roche QMS and governance requirements.
- Experience supporting globally distributed teams and driving alignment across multiple organizations, platforms, and strategic initiatives.
Wer wir sind
Eine gesündere Zukunft treibt uns zur Innovation an. Mehr als 100.000 Mitarbeiter weltweit arbeiten gemeinsam daran, wissenschaftliche Fortschritte zu erzielen und sicherzustellen, dass jeder Zugang zur Gesundheitsversorgung hat – heute und für zukünftige Generationen. Durch unser Engagement werden über 26 Millionen Menschen mit unseren Medikamenten behandelt und mehr als 30 Milliarden Tests mit unseren Diagnostik-Produkten durchgeführt. Wir ermutigen uns gegenseitig, neue Möglichkeiten zu erkunden, Kreativität zu fördern und hohe Ziele zu setzen, um lebensverändernde Gesundheitslösungen zu liefern.
Gemeinsam können wir eine gesündere Zukunft gestalten.
Roche ist ein Arbeitgeber, der die Chancengleichheit fördert.
