Sr Manager, IMSC Information Architecture Make/Quality

At Johnson & Johnson, we believe health is everything.

Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, andsolutions are personal.

Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

Learn more at jnj. com .

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We provide an inclusive work environment where each person is considered as an individual.

At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function

Data Analytics & Computational Sciences

Job Sub Function

Data Governance & Policy

Job Category

Professional

All Job Posting Locations

Latina, Italy, Leiden, Netherlands, Madrid, Spain, Ringaskiddy, Cork, Ireland, Zug, Switzerland

Job Description

Johnson & Johnson is recruiting for a Sr Manager, IMSC Information Architecture Make/Quality , located in Zug, CH.

As we continue to build out our Data Strategy and Operations organization within Innovative Medicine Supply Chain, we are creating this role as part of the Information Architecture pillar.

The role will help establish Information Architecture as a strategic capability that enables AI-ready, trusted, reusable, and context-rich information across IMSC domains.

The Sr Manager, IMSC Information Architecture Make/Quality will lead and oversee information architecture with primary focus on Make and Quality functional domains .

This role will ensure that data assets are not only technically well designed, but also semantically aligned, discoverable, reusable, governed, and ready for analytics, automation, and AI.

As Sr Manager, IMSC Information Architecture, you will ensure data assets are FAIR, AI-ready, and aligned with IMSC Information Architecture principles, standards, metadata requirements, semantic models, and the broader IMSC information architecture.

You will develop and apply strategies to minimize duplication of information, clarify source of truth, establish shared business meaning, and strengthen consistency across supply chain functions, systems, and data asset s.

A key focus will be building the business context layer that connects data, knowledge, metadata, taxonomy, ontology, and semantic relationships to enable trusted AI and better business outcomes.

Your scope will span master data, transactional data, analytical data, metadata, business glossary definitions, semantic models, taxonomies, ontology structures, and knowledge graph patterns.

You will help define how data is structured, described, connected, governed, and reused across the data asset lifecycle.

You will collaborate closely with functional business partners, Data Owners, Data Governance, Data Operations, J & J Technology, and ensure adherence to architecture partners to design scalable information models and data asset s that maximize business value, improve data trust and discoverability, and create the foundation for AI-ready data across IMSC.

Key Responsibilities

• Design and implement end-to-end FAIR and AI-ready data assets for Make/Quality functional domains, spanning master data, transactional data, operational data, and analytics consumption needs.
• Lead conceptual and logical information modelling for assigned domains, ensuring that data assets reflect consistent business meaning, reusable structures, clear ownership, and alignment with IMSC Information Architecture standards.
• Define and apply Information Architecture principles, standards, and patterns across metadata, taxonomy, semantic modelling , ontology, business glossary, data assets design, and context layer development.
• Establish and maintain domain-level business semantics, including shared definitions, business terms, relationships, classifications, naming conventions, and mappings between business concepts, master data entities, and system representations.
• Support the development of the IMSC AI-ready context layer by connecting metadata, business meaning, relationships, rules, lineage, and knowledge structures that make data understandable and usable for analytics, automation, and AI.
• Partner with Data Owners and business process owners to ensure business ownership of data meaning, shared definitions, domain taxonomies, and reusable information models.
• Work closely with Data Governance to ensure that semantic definitions, metadata standards, data asse t requirements, and information architecture policies are governed, adopted, and embedded into the data asse t lifecycle.
• Collaborate with Data Operations and J & J Technology partners to ensure information models are translated into scalable, technically feasible, and governed data asse ts within the approved IMSC data ecosystem.
• Provide Information Architecture oversight for strategic initiatives and architecture reviews, ensuring that new data assets are aligned to standards for meaning, ownership, reuse, metadata, discoverability, governance, and AI readiness.
• Contribute to the design and adoption of the IMSC Information Architecture operating model, including roles, responsibilities, decision rights, review criteria in architecture board , li fecycle processes, and federated ways of working.
• Support the establishment and adoption of metadata cataloguing practices, business glossary standards, lineage visibility, data contrac ts, and reusable templates that improve discoverability and trust.
• Lead ontology and knowledge graph implementation to create a coherent context layer.
• Identify and pilot opportunities to use AI-assisted and agentic AI capabilities to accelerate metadata curation, semantic modelling , taxonomy development, ontology creation, data asse t design, and quality review processes.
• Create practical guidance, playbooks, and examples that help business and technology teams understand and apply Information Architecture standards across domains.
• Drive adoption and awareness of Information Architecture across IMSC by simplifying complex concepts, communicating the value of metadata and semantics, and supporting education for business and technology stakeholders.

Qualificatio n s

• Experience working in pharmaceutical, healthcare, life sciences, supply chain, manufacturing, quality, or regulated data environments.
• Good knowledge of industry standards and practices related to data management and information architecture, including FAIR, IDMP, ISO standards, data mesh, domain-driven design, semantic layers, and knowledge graphs.
• In-depth experience in information architecture, data architecture, data modelling , or data management, with strong capability in conceptual and logical modelling across complex enterprise domains.
• Strong understanding of FAIR data principles, data product design, metadata management, business glossary, taxonomy, semantic modeling, ontology concepts, and knowledge graph patterns.
• Experience designing end-to-end data product s across master data, transactional data, operational data, and analytics consumption layers.
• Strong understanding of how information architecture enables AI-ready data, including semantic consistency, contextual connectivity, metadata completeness, lineage, data ownership, and governed reuse.
• Experience working with data catalogues, metadata repositories, business glossaries, ontology tools, semantic layers, knowledge graph technologies, lakehouse platforms, or comparable data and information management capabilities.
• Familiarity with platforms and capabilities such as Alation, Databricks, Neo4j, Top Braid , SAP S/4HANA, SAP BDC, medallion architecture, or comparable enterprise data and semantic technologies.
• Ability to translate business needs into reusable information structures, shared definitions, domain models, data product requirements, and practical design guidance.
• Experience working in a federated operating model, partnering with Data Owners, Data Governance, Data Operations, and technology delivery teams.
• Experience supporting multi-year strategy, transformation, or technology deployment initiatives with focus on information management, data management, data products, analytics, or AI enablement.
• Strong ability to operate across business and technology stakeholders, simplify complex technical topics, and communicate the value of information architecture to senior and cross-functional audiences.
• Proven track record of building partnerships, influencing without authority, inspiring change, and delivering results across complex global ecosystems.
• Experience creating standards, playbooks, templates, architecture review criteria, or adoption materials for business and technology audiences.
• Good knowledge and understanding of data governance, data quality, data contracts, data product certification, lineage, access controls, and operating models for governed data products.
• Good knowledge and understanding of using automation, AI-assisted workflows, or agentic AI capabilities to accelerate data modelling , metadata curation, semantic modelling , or data quality processes.
• Energetic, accountable, curious, collaborative, and committed to continuous learning and capability building.

Other

• Requires up to 10% of international travel

Required Skills

• Advanced Analytics
• Analytics Dashboards
• Business Behavior
• Database Management
• Data Governance
• Data Management
• Data Privacy Standards
• Data Savvy
• Data Security
• Data Stewardship
• Emerging Technologies
• Information Security Risk Management
• Organizing
• Performance Measurement
• Standard Operating Procedure (SOP)
• Strategic Thinking
• Technical Credibility

Preferred Skills

• Advanced Analytics
• Analytics Dashboards
• Business Behavior
• Database Management
• Data Governance
• Data Management
• Data Privacy Standards
• Data Savvy
• Data Security
• Data Stewardship
• Emerging Technologies
• Information Security Risk Management
• Organizing
• Performance Measurement
• Standard Operating Procedure (SOP)
• Strategic Thinking
• Technical Credibility
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