Director, AI Data Architect
Pfizer INC · 🌍 Greece-Thessaloniki Chortiatis
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About the role
Join Pfizer International Commercial Division, Business Transformation organization to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues, patients, and physicians. Our team of data engineering, data science, and AI professionals is at the forefront of Pfizer’s transformation into a digitally driven organization, using data science and AI to change patients’ lives, leading process and engineering innovations to advance AI and data science applications from prototypes and MVPs to full production. As the AI Data Architect you will be the senior individual-contributor authority for AI-ready data across International Commercial. You will define what "AI ready" means in practice: designing the data models, semantic layers and readiness standards that allow analytical and agentic AI solutions to consume commercial data reliably, safely and at scale. This role owns the architecture and modelling backbone of the AI data product portfolio: dimensional and semantic model design, AI readiness assessment frameworks, and data product blueprints, from source analysis through to certified, AI-consumable data assets. This is a hands-on, individual contributor role with no people management responsibilities. You will influence through architectural leadership: setting standards, reviewing designs, and enabling engineering pods and market teams to build AI-ready data products with speed, quality and consistency. What You Will Achieve 1) AI Data Readiness · Own and evolve the AI readiness framework and criteria used to assess, certify and prioritise datasets for AI consumption across international markets · Conduct AI readiness assessments of commercial data sources and define remediation roadmaps (data quality, metadata, semantics, access) in partnership with market and platform teams · Extend AI readiness beyond structured data: define architectures and enrichment strategies (parsing, chunking, metadata, embeddings) that make unstructured content such as documents, transcripts and field notes AI-consumable · Define reusable standards, blueprints and accelerators that enable market teams to achieve AI readiness independently · Stay current with emerging AI data architecture patterns (semantic layers, agentic data access, retrieval) and evaluate their applicability to the International Commercial roadmap 2) Data Modelling & Semantic Layer Design · Design dimensional and analytical data models (fact and dimension design, grain definition, surrogate key and conformed dimension strategy) optimised for both human analytics and AI/agentic consumption · Architect and govern semantic layers, business ontologies and metric definitions that ensure consistent business meaning across markets, brands and data products · Model knowledge graphs and graph-based representations (entities, relationships, ontologies) where connected data improves discovery, retrieval and agent reasoning · Establish modelling standards for AI consumption: naming conventions, documentation, lineage, constraints and machine-readable metadata · Resolve complex cross-source integration and dimension-alignment challenges (master data, identifiers, encodings) spanning global and local market datasets 3) Solution & Architecture Design · Translate business and AI use-case requirements into target-state data architectures and data product designs · Design semantic search and retrieval architectures: embedding pipelines, vector storage and hybrid retrieval (keyword, vector and graph) that ground AI agents in governed enterprise data · Partner with Product Owners, data engineers and AI engineers to ensure designs are implementable, performant and pro