Senior Manager, Data Engineering & Semantics

1. Role Summary The Senior Manager is a capability leader within Data Engineering & Semantics, owning one or more capability areas — such as Data Engineering, Data Product Management or Data Trust & Reliability Engineering — while retaining sufficient breadth to lead across the team mandate and complex CEI PODs. The role turns DSM-led business requirements into trusted, governed and AI-ready data, including the CL layer of the AI Empowered Consumption Layer (AICL), so that reporting, analytics and AI solutions work from one consistent data foundation. 2. Key Accountabilities Own one or more team capability areas end to end, defining capability roadmaps, engineering standards, reusable patterns, controls, service expectations and lifecycle outcomes across China CEI. Lead and govern Data Engineering across L1–L4 — ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data, and reporting and business intelligence foundations. Lead technical management and lifecycle governance of BI platforms, including workspace and environment administration, access controls, release management, refresh reliability, performance, capacity and license utilization; partner with Enterprise Engineering for production operations and implement GCA DSM-approved business definitions and KPIs. Lead and govern Data Product Management — reusable, governed data products and data agents, including the CL layer of the AI Empowered Consumption Layer (AICL) and agent-facing data contracts. Lead and govern Technical Semantics — semantic models, metadata, taxonomies and technical mappings that implement DSM-approved business definitions and harmonized KPIs. Lead and govern Data Trust & Reliability Engineering — technical data governance controls, shift-left data quality checks, lineage, observability and China data, privacy and cross-border controls. Serve as senior design authority for complex data products, semantic architectures and data-reliability solutions, and lead data contributions to complex PODs from MVP scoping through scale. Shape capability capacity, skills mix and partner leverage across internal, PIC and external resources; manage material risks and dependencies; and mentor Managers, Senior Associates and specialists. 3. Required Capabilities & Competencies (a) Critical Capabilities & Qualifications Modern data engineering, data architecture, data products and reporting and business intelligence foundations. Demonstrated hands-on expertise in at least one major BI platform (e.g. Microsoft Power BI, Tableau or Qlik Sense), including platform administration, SQL, dimensional and semantic modeling, data connectivity and refresh, row-level security, dashboard development and performance tuning; proficiency in relevant platform-specific technologies (e.g. DAX and Power Query for Power BI) and governed self-service BI enablement. 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. Bachelor’s degree or above in Computer Science, Data Engineering, Data Science, Information Systems or a related computing discipline. 10+ 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. (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 GCA DSM, AITO, Enterprise Engineering, PIC and CEI PODs. Precision and learning: communicates clearly, documents decisions, uses evidence and telemetry and continuously improves delivery. 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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