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 production grade

·      Align designs with enterprise Data and AI Platform standards (governance, security, cataloguing) while maintaining product delivery velocity

4) Technical Leadership & Enablement

·      Act as the subject-matter expert and design authority for AI-ready data: lead design reviews, author architecture decision records (ADRs) and reference architectures

·      Coach and upskill data engineers and market teams on modelling and AI readiness best practices, without direct people management

·      Champion engineering and data quality excellence through documentation, patterns and continuous learning (labs, guilds, demos)

Here Is What You Need (Minimum Requirements)

·      Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or related field

·      10+ years in data architecture, data engineering or analytics engineering, with a proven track record as a senior individual contributor

·      Deep expertise in data modelling: dimensional modelling (Kimball), normalized and analytical schema design, grain and surrogate key strategy

·      Strong hands-on experience designing AI-ready data products and semantic layers end-to-end

·      Experience architecting for unstructured data (documents, text, transcripts, images): extraction, chunking, enrichment and metadata strategies for AI consumption

·      Proficiency in SQL, Python with practical experience in Snowflake

·      Experience with cloud platforms (AWS or Azure), modern data stack tooling (dbt, Airflow) and CI/CD (GitHub Actions)

·      Experience with AI ready data enablement frameworks and practical implementations, Semantics management and Enterprise data catalogues (Collibra)

·      Experience with complex data sources including anonymized patient data (EMR/Claims)

·      Strong English communication skills (written and verbal); ability to work across global time zones

 

PREFERRED QUALIFICATIONS

·      Advanced degree (MS/PhD) in Computer Science, Data Engineering, or Data Science

·      Experience enabling LLM and agentic data access patterns (semantic views, text-to-SQL agents, retrieval-augmented generation)

·      Experience with vector databases and embedding stores (e.g. Snowflake Cortex Search, pgvector, Pinecone, OpenSearch) including hybrid search, re-ranking and evaluation of retrieval quality

·      Experience with graph databases and knowledge graphs (e.g. Neo4j, Amazon Neptune; property graph or RDF/OWL modelling, GraphRAG patterns)

·      Experience with data governance and master data management in a global organisation

·      Experience with BI/visualization tools (Tableau, Power BI, Streamlit)

·      Experience in regulated/compliance-aware environments (GxP, HIPAA, SOC2)

·      Certifications: Snowflake, AWS/Azure Professional

Work Location Assignment: Hybrid

Please apply by sending your CV and a motivational letter in English 

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To learn more about acceptable and prohibited uses of AI during the recruitment process, please review our candidate AI-use guidelines available on Pfizer Careers.

 

 

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