Manager, Data Engineering & Semantics

1. Role Summary

The Manager is a delivery owner within Data Engineering & Semantics, and may be assigned to a specific capability or deployed into a cross-functional CEI POD based on portfolio priorities.

The role combines hands-on data engineering, data product and data quality delivery with backlog, quality, readiness and stakeholder accountability, including delivery of the AICL CL layer.

2. Key Accountabilities

• Manage day-to-day delivery for assigned data engineering, data product, semantic or data-quality outcomes.

• Own day-to-day delivery of Data Engineering across L1–L4 — ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data, and reporting and business intelligence foundations.

• Own day-to-day delivery of reusable, governed data products and data agents, including the CL layer of the AI Empowered Consumption Layer (AICL) and agent-facing data contracts.

• Own day-to-day delivery of technical semantics — semantic models, metadata, taxonomies and technical mappings that implement DSM-approved business definitions and harmonized KPIs.

• Own day-to-day delivery of Data Trust & Reliability Engineering — technical data governance controls, shift-left data quality checks, lineage, observability and China data, privacy and cross-border controls.

• Maintain clear backlog, dependencies, risks, acceptance criteria, test evidence, documentation and transition plans.

• Coordinate with GCA DSM on definitions and issue resolution, with Enterprise Engineering, PIC and partners on productionization, and coach Senior Associates.

3. Required Capabilities & Competencies

• Modern data engineering, data architecture, data products and reporting and business intelligence foundations.

• Hands-on proficiency in one or more mainstream BI platforms (e.g. Microsoft Power BI, Tableau, Qlik Sense or SAP Analytics Cloud), including data connectivity and integration, semantic modeling, dashboard and report development, access controls, refresh management and performance optimization, implementing DSM-approved business definitions and KPIs.

• Semantic modeling, metadata, taxonomy and ontology; knowledge and unstructured data engineering; data enablement for AI retrieval and agents.

• Technical data governance, data quality, lineage and observability, and China data privacy and cross-border controls.

• 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 GCA DSM, AITO, Enterprise Engineering, PIC and CEI PODs.

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

• Bachelor’s degree or above in Computer Science, Data Engineering, Data Science, Information Systems or a related computing discipline.

• 8+ years of progressive relevant experience based on a bachelor’s degree, where a master’s or doctoral degree may reduce the years required, with scope and judgment consistent with this role.

• Agile delivery, product metrics, adoption measurement and evidence-based iteration. Agile and product certifications are preferred (e.g. CSPO / PSPO, CSM / PSM, and SAFe POPM or SAFe Agilist for scaled-agile environments).

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

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

4. Key Collaboration

The role works with the Director, Data Engineering & Semantics and team colleagues, CEI PODs and PIC. GCA Data Strategy & Management (DSM) owns AICL business requirements, business definitions, unified KPIs, master data business rules and business stewardship; this team owns the corresponding technical implementation and the AICL CL layer. The role delivers AICL as one POD with the AITO AI engineering team (AI layer) and GCA DSM, works with AITO AI Platform & Foundation on data access to the China AI platform, partners with Enterprise Engineering, Deployment & Operations for production integration, and engages global data teams and privacy, legal and compliance stakeholders.

5. Measures of Success

• Timely, high-quality delivery of governed, reusable data products, semantic models and the AICL CL layer.

• Consistent implementation of DSM-approved business definitions and harmonized KPIs across reporting, analytics and AI.

• Improved data quality, lineage, observability and adherence to China data and privacy requirements.

• Faster POD delivery through trusted, reusable and AI-ready data.

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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