Pfizer Jobs

Senior Manager, Data Engineering & Semantics

Pfizer

Senior Manager, Data Engineering & Semantics

Posted An Hour Ago

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4 Locations

In-Office

Senior level

Artificial Intelligence • Healthtech • Machine Learning • Natural Language Processing • Biotech • Pharmaceutical

We’re in relentless pursuit of breakthroughs that change patients’ lives.

The Role

Leads data engineering, data product management, semantic modeling, BI platform governance, and data trust and reliability across China CEI. Owns capability roadmaps, technical standards, governed data products, AI-ready data foundations, metadata, lineage, observability, privacy controls, and cross-border data requirements. Serves as senior design authority, manages capacity and risks, partners across business and technology teams, and mentors data professionals.

Summary Generated by Built In

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.
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 .
Information & Business Tech

Skills Required

Bachelor's degree or higher in Computer Science, Data Engineering, Data Science, Information Systems, or a related computing discipline
10+ years of progressive relevant experience with a bachelor's degree, or reduced experience with a master's or doctoral degree
Modern data engineering, data architecture, data products, reporting, and business intelligence experience
Hands-on expertise with a major BI platform such as Microsoft Power BI, Tableau, or Qlik Sense
Experience with BI platform administration, SQL, dimensional and semantic modeling, data connectivity and refresh, row-level security, dashboard development, and performance tuning
Proficiency in relevant platform technologies such as DAX and Power Query for Power BI
Experience with semantic modeling, metadata, taxonomies, ontologies, and knowledge or unstructured data engineering
Experience enabling data for AI retrieval and agents
Technical data governance, data quality, lineage, observability, China data privacy, and cross-border controls
Agile delivery, product metrics, adoption measurement, and evidence-based iteration
Ability to work across business, product, technology, data, and governance stakeholders in a matrixed environment
Pharmaceutical or other highly regulated industry experience
China technology, data, privacy, or vendor-environment experience
Agile or product certifications such as CSPO, PSPO, CSM, PSM, SAFe POPM, or SAFe Agilist

What the Team is Saying

Pfizer Compensation & Benefits Highlights

Healthcare Strength — Healthcare coverage is described as comprehensive, with multiple plan options, dental and vision, mental-health services, telehealth, and eligible Pfizer medications at no cost. Family-building benefits such as infertility treatment, adoption, and surrogacy support further reinforce the offering.
Retirement Support — Retirement support includes a 401(k) with employer matching plus an additional company retirement contribution, alongside access to financial-planning resources. This combination is positioned as a meaningful part of total rewards.
Leave & Time Off Breadth — Paid time off includes 15 holidays, three to six weeks of vacation depending on service, and the option to purchase additional vacation weeks, plus dedicated caregiver-leave days. Twelve weeks of paid parental leave and backup care resources add flexibility for family needs.

Learn more about Pfizer's Compensation & Benefits →

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The Company

HQ: New York, NY

121,990 Employees

Year Founded: 1848

What We Do

Our purpose ensures that patients remain at the center of all we do. We live our purpose by sourcing the best science in the world; partnering with others in the healthcare system to improve access to our medicines; using digital technologies to enhance our drug discovery and development, as well as patient outcomes; and leading the conversation to advocate for pro-innovation/pro-patient policies.

Why Work With Us

We are the inventors, the problem solvers, the big thinkers — those who surmount any hurdle to deliver breakthrough medicines to the people who are counting on them the most.