Novartis

Overview

Lead cross-domain Data & Platform architecture and semantic architecture to establish consistent, trusted, reusable, and machine-interpretable data across the enterprise.

About Novartis

Novartis is a medicines company focused on reimagining medicine to improve and extend people's lives, with a vision to become the most valued and trusted medicines company in the world.

Requirements & Eligibility

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline; advanced degree or relevant certifications preferred.
• Approximately 12+ years of experience in Data & Platform Architecture, Information Architecture, Data Management, Analytics, Data Modeling, or Enterprise Architecture within large global organizations.
• Strong expertise in conceptual and logical data modeling, enterprise information architecture, metadata management, data governance, data quality, and data integration.
• Proven experience designing enterprise semantic models, ontologies, taxonomies, knowledge graphs, governed metrics, and semantic interoperability frameworks.
• Deep understanding of AI-ready data architecture, data products, data contracts, metadata, lineage, and advanced analytics enablement.

Key Responsibilities

• Define and govern enterprise-wide data, platform, and semantic architecture standards, principles, reference models, and roadmaps aligned to business strategy and target-state architecture.
• Lead the design and maintenance of enterprise information models, conceptual and logical data models, canonical entities, and cross-domain data relationships to ensure consistency and interoperability.
• Establish and evolve the Enterprise Semantic Layer through semantic models, ontologies, taxonomies, knowledge graphs, governed metrics, and reusable business definitions.
• Design AI-ready data and knowledge architectures that enable GenAI, Agentic AI, advanced analytics, RAG, and knowledge-driven decision-making through trusted and governed data assets.
• Drive data modeling, metadata management, data product enablement, and traceability across business concepts, semantic definitions, physical data assets, and consuming solutions.