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 stakeholdersincluding 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 bachelors degree with at least 4 years ofexperience; OR a masters degree with at least 2 years of experience; ORa PhD with 0 years of experience; OR as associates degree with 8 yearsof experience; OR a high school diploma (or equivalent) and 10 years ofrelevant 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

• Bachelors 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

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 onPfizer Careers.