Director, Data Engineering & Semantics — China

Role Snapshot

Function / Team: Commercial Excellence & Innovation (CEI) - Digital & Technology (Data Engineering & Semantics)

Location:  China

Grade / Level: Director

Reports to: Digital & Technology Head (D&T Head)

1. Role Summary

The Director leads the Data Engineering & Semantics capability that provides trusted, governed and AI-ready data for analytics, digital products and AI solutions across Pfizer China commercial operations.

The role owns data engineering, reporting and business intelligence foundations, knowledge and unstructured data engineering, technical semantic models, technical data quality and observability, technical data governance controls, reusable data agents, and AI Consumption Layer (AICL) delivery. It translates business definitions, harmonized KPIs, master data business rules and business data governance requirements into scalable technical solutions. This leader ensures CEI PODs work from one consistent and compliant data foundation, aligned with enterprise data standards and China data and privacy requirements.

2. Key Accountabilities

• Define and execute the China technical roadmap for data engineering and semantic implementation, aligned with DSM-led business requirements, CEI priorities, enterprise data strategy and global Digital & Technology standards.

• Lead standardized data engineering across ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data.

• Translate business definitions, harmonized KPIs and taxonomies into reusable semantic models, metadata and technical mappings across reporting, analytics and AI.

• Build reusable data products, reporting and business intelligence foundations and data agents that improve discoverability, interoperability and reuse.

• Own AICL technical delivery against business requirements and priorities, connecting governed data, semantic models and knowledge assets with the China LLM foundation for consistent, secure and scalable consumption by business teams and PODs.

• Implement technical data governance controls, lineage, observability and data quality checks based on business governance requirements; resolve technical issues and support DSM-led business stewardship.

• Ensure data handling meets China data, privacy and cross-border requirements, partnering with privacy, legal and compliance stakeholders.

• Enable AITO and CEI PODs with trusted data, technical implementation of semantic standards and data engineering specialists from MVP scoping through scale.

• Lead and develop data engineering, technical semantics and technical data governance talent, balancing foundation build with priority business demand.

3. Required Capabilities & Competencies

(a) Critical Capabilities & Qualifications

• Enterprise data strategy, modern data engineering, data architecture, data products and reporting and business intelligence foundations.

• Semantic modeling, metadata, taxonomy and ontology; technical integration of master and reference data in line with DSM-defined business rules; and knowledge and unstructured data engineering.

• Technical implementation of business data governance requirements, support for business stewardship, data quality controls, lineage, observability and China data privacy and cross-border controls.

• Data enablement for AI, including retrieval, knowledge grounding, data access for agents, reusable data agents and AI consumption layer delivery.

• Bachelor’s degree in Computer Science, Data Engineering, Data Science, Information Systems or a closely related discipline; an advanced technical or business degree is preferred.

• Typically 15+ years of progressive relevant experience, including several years leading teams and delivery at scope and complexity appropriate to similar level.

• Demonstrated ability to work across business, product, technology, data and governance stakeholders in a matrixed environment.

• Experience in a highly regulated industry and the China technology, data, privacy or vendor environment is preferred.

(b) Leadership Expectations

• Outcome ownership: connects decisions to measurable business, user, service and transformation outcomes.

• AI-first and enterprise-minded: applies responsible AI, reuse, architecture, security, privacy and scale principles appropriate to the work.

• POD collaboration: enables clear accountability, effective cross-functional execution and minimal handoff friction across peer teams and partners.

• Precision and learning: communicates clearly, documents decisions, uses evidence and telemetry and continuously improves delivery.

• Talent development: grows data engineering, technical semantics and technical data governance leadership across the team.

4. Key Collaboration

The Director maintains strong partnership with Go-to-Market Transformation (GTMx), AI Transformation Office (AITO), Global Commercial Analytics (GCA), Commercial Excellence (CE), Pfizer Innovation Center (PIC) and peer D&T capabilities, to connect trusted data to measurable commercial outcomes. The role works closely with AI Platform & Foundation on AICL delivery on top of the China LLM foundation, retrieval, knowledge grounding and agent data access, and with Enterprise Engineering, Deployment & Operations on production integration and support of data products. It also works with global data and analytics teams, data owners and stewards, and privacy, legal, risk and compliance stakeholders.

Data Strategy Management (DSM) within GCA owns the business side of AICL, including business requirements, priorities, business acceptance and business operations, as well as KPI harmonization, master data business ownership and business data governance. This role owns the corresponding technical implementation and data engineering delivery, working with DSM on business definitions, stewardship and issue resolution.

5. Measures of Success

• Technical delivery quality and reuse of governed data products, semantic models and data agents, with consistent implementation of DSM-approved business definitions and harmonized KPIs.

• Improved data quality, lineage, observability and semantic consistency across reporting, analytics and AI.

• Faster POD delivery through trusted, reusable and AI-ready data foundations, timely AICL technical delivery against agreed business requirements, and reliable platform performance.

• Effective implementation of DSM-approved data standards and technical controls, with timely resolution of engineering issues and adherence to applicable China data and privacy requirements.

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.

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