Manager, Data Specialist

Role Overview

• Engage directly with commercial business stakeholders 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.
• 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.
• 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.
• 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.
• Identify and document data lineage, ownership, and usage policies for commercial data domains.
• Understand and support the data requirements of AI/ML and advanced analytics use cases.

Requirements

• 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 an 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.

Tech Stack

• Amazon Redshift
• ETL
• SQL

Benefits

• 401(k) plan with Pfizer Matching Contributions
• Additional Pfizer Retirement Savings Contribution
• Paid vacation
• Holiday and personal days
• Paid caregiver/parental and medical leave
• Health benefits including medical, prescription drug, dental, and vision coverage