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 analytics use cases, including feature engineering inputs, model training datasets, and inferencing pipelines. 

• Coordinate with data scientists and ML engineers to ensure data products are structured, enriched, and accessible in formats optimized for model development and deployment. 

• Contribute to the continuous improvement of data standards that ensure commercial AI initiatives are built on a trusted, governed data foundation. 

 

Basic Qualifications 

• Applicant must have a bachelor's degree with at least 4 years of experience; OR a master's degree with at least 2 years of experience; OR a PhD with 0+ years of experience; OR as associate's degree with 8 years of experience; OR a high school diploma (or equivalent) and 10 years of relevant experience.

• Experience in a data-focused role such as business analyst, data analyst, data product manager, or comparable function within a data-intensive organization. 

• Demonstrated experience translating business requirements into technical specifications for data engineering or BI/analytics development teams. 

• Experience in the pharmaceutical, biotech, or life sciences industry, particularly within a commercial analytics or sales operations function. 

• Familiarity with commercial pharma data sources such as IQVIA (APLD, NPA, NSP), Symphony Health, Veeva CRM, patient claims data, or HCP/HCO affiliation data. 

• Working knowledge of relational data concepts, SQL, and data warehousing/lakehouse architectures (e.g., Snowflake, Databricks, Redshift). 

• Familiarity with data pipeline development concepts (ETL/ELT), data modeling, and data quality frameworks. 

• Experience working within an Agile/Scrum delivery model; proficiency with tools such as Jira, Confluence, or equivalent. 

• Strong written and verbal communication skills with the ability to interface effectively with both technical and non-technical audiences. 

Preferred Qualifications

• Bachelor's degree in a quantitative, analytical, or business discipline (e.g., Data Science, Information Systems, Statistics, Business Analytics). 

• 5+ years of experience in a data-focused role such as business analyst, data analyst, data product manager, or comparable function within a data-intensive organization. 

Work Location Assignment: Hybrid


The annual base salary for this position ranges from $99,200.00 to $165,400.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 12.5% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States.



This role is posted in multiple locations. If you are applying for the role in an secondary job posting location where pay transparency regulations apply, your Talent Advisor will share the local pay information with you during the first interview.




Relocation assistance may be available based on business needs and/or eligibility.




Candidates must be authorized to be employed in the U.S. by any employer.

U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future.






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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