Manager, Data Specialist

ROLE SUMMARY 

The AI Acceleration (AIA) function within the Chief Marketing Office (CMO) is the single, business-led engine that owns the design, delivery, and scale-up of priority AI capabilities across Commercial operations. AIA works in tight collaboration with various Pfizer functions to deploy and maintain production-grade AI solutions that simplify how we work and drive measurable value across all processes. 

The Manager, Data Specialist will serve as a critical bridge between business stakeholders and the data engineering team. This role will be responsible for deeply understanding commercial business processes, translating complex analytical and AI/ML requirements into actionable data engineering specifications, and ensuring the delivery of high-quality, governed data products that power commercial insights and AI-driven decision-making. 

The Manager, Data Specialist will partner closely with business translators, commercial analytics leads, data scientists, and AI/ML engineers to define data needs, validate data availability, and ensure that the data engineering team is building solutions that are fit for purpose, well-documented, and aligned to enterprise data governance standards. 

ROLE RESPONSIBILITIES 

Requirements Translation & Stakeholder Partnership 

• Engage directly with commercial business stakeholders—including Brand, Sales Operations, Market Access, Content Generation, and Medical —to elicit, clarify, and document data and analytics requirements. 

• Decompose high-level business needs into structured data engineering work items including data product definitions, pipeline specifications, transformation logic, and acceptance criteria. 

• Facilitate working sessions between business users and engineering teams to align on scope, timelines, and technical feasibility. 

• Serve as the primary point of contact for data-related inquiries from commercial analytics and AI/ML teams, triaging requests and ensuring efficient backlog management. 

Data Product Definition & Documentation 

• Author detailed data product specifications including source-to-target mappings, business rules, data dictionaries, and field-level definitions. 

• Define and document data quality expectations, validation rules, and SLA requirements in collaboration with data engineering and governance teams. 

• Maintain and continuously improve data documentation artifacts within the enterprise data catalog to support self-service discovery and AI-readiness. 

• Partner with data architects to ensure proposed data products align with the enterprise semantic layer and are optimized for downstream AI/ML and analytics consumption. 

Data Engineering Backlog Management 

• Translate prioritized business requirements into well-defined user stories, epics, and technical tasks within the data engineering development backlog. 

• Collaborate with data engineers to refine tickets, clarify ambiguities, and provide domain context that enables efficient, high-quality delivery. 

• Track progress of data engineering deliverables, identify blockers, and communicate status and impacts to business stakeholders in a clear, non-technical manner. 

• Validate delivered data products against requirements and coordinate user acceptance testing with business teams prior to production release. 

Data Quality & Governance 

• Champion data quality by defining, monitoring, and communicating metrics that measure the reliability, completeness, and timeliness of commercial data assets. 

• Work with data governance and compliance teams to ensure data products adhere to applicable privacy, regulatory, and data stewardship standards relevant to commercial pharma (e.g., IQVIA, CRM, HCP/HCO data). 

• Identify and document data lineage, ownership, and usage policies for commercial data domains. 

AI & Analytics Enablement 

• Understand and support the data requirements of AI/ML and advanced analy