Manage engineering backlog, estimates, dependencies, risks, reviews, testing, release readiness and stakeholder communication.
Coordinate PIC, vendor and systems-integrator engineers, verify deliverables and coach Senior Associates.
3. Required Capabilities & Competencies
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.
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.
Bachelor’s degree or above in Computer Science, Software Engineering, Information Technology, Engineering 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.
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
It delivers within the platform standards and AI platform technical qualification requirements owned by AITO AI Platform & Foundation; manages solution and application engineering, product deployment and production readiness; 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.
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