1. Role Summary

The Senior Manager provides senior technical and delivery leadership within Enterprise Engineering, Deployment & Operations, owning one or more capability areas — such as Enterprise Engineering & Product Deployment, Run & BAU and Vendor Management, or Enterprise Architecture — while retaining breadth across the team mandate.

The role turns architecture and product direction into executable engineering roadmaps and reusable practices that move AITO-validated and POD solutions into secure, scalable and maintainable production.

2. Key Accountabilities

• Own one or more team capability areas end to end, including roadmap, standards, engineering delivery, controls, service expectations and lifecycle outcomes.

• Lead and govern enterprise solution architecture and production engineering, ensuring applications, integrations, data services and AI-enabled products conform to enterprise and China AI platform reference architectures.

• Lead and govern enterprise engineering and product deployment — software, integration, API, automation and solution engineering; product-level CI/CD, production deployment, scale-up, application SRE, security, cost and technical observability, including AI-enabled business applications on the China AI platform.

• Lead and govern Shared, deployable engineering pool — integrating internal engineers, PIC, systems integrators and contractors into accountable POD and product teams.

• Lead and govern product and application Run & BAU — incidents, releases, access, service performance, operational readiness and business communications — and own the production-readiness and go-live gate, following completion of the AI platform technical qualification gate where applicable.

• Lead and govern Vendor management — China-compliant local vendors, solution delivery contracts and compliant contracting execution.

• Lead complex POD engineering, resolve technical dependencies and ensure a clean transition from AITO-validated MVP into production and BAU.

• Coordinate internal engineers, PIC, systems integrators and contingent resources as one delivery team, and mentor Managers and engineers.

3. Required Capabilities & Competencies

(a) Critical Capabilities & Qualifications

• Enterprise engineering across software, integration, API, automation and solution engineering, and enterprise and solution architecture.

• Reusable patterns, code and design quality, automated testing, scalability, resilience, maintainability and technical debt.

• DevSecOps, CI/CD, product deployment, SRE, security, cost and technical observability, Run and BAU, and vendor and systems integrator management.

• Bachelor’s degree or above in Computer Science, Software Engineering, Information Technology, Engineering 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.

• 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 Business Units, GTMx, GCA, CE, AITO, PIC and peer D&T capabilities.

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

4. Key Collaboration

It delivers within the platform standards and AI platform technical qualification requirements owned by AITO AI Platform & Foundation; owns solution and application architecture, product deployment, production readiness and go-live; works with Data Engineering & Semantics on production data integration; engages Business Units, GTMx, GCA and CE; and works with global engineering teams, governance functions, vendors and systems integrators.

5. Measures of Success

• Predictable transition from validated MVP to compliant production and scale, with a named BAU owner for every scaled product.

• Improved engineering quality, reuse, automated testing, reliability, performance and maintainability.

• Lower delivery friction and clearer accountability across internal teams, PIC and partners.

• Reliable Run & BAU with transparent risks, technical debt, cost and service-health measures.

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

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.