AI Data Engineering (Secondment 12-18 Months)

Pfizer•Kalamazoo, MI

5d•Onsite

About The Position

The AI Data Engineer is responsible for building and maintaining the trusted data foundations required for AI, analytics, automation, and operational decision support across the Kalamazoo site. This role connects data from manufacturing and business systems, builds reliable data pipelines, creates reusable data models, and supports the technical enablement needed to move AI opportunities from concept to scalable business value. This role partners closely with Manufacturing, Supply Chain, Quality, Engineering, Digital, Automation, IMEx/CI, Data Science, and Compliance partners to ensure data is available, reliable, governed, and fit for use. The role is a hands-on builder and technical translator who helps convert business problems into usable data products and integration patterns.

Requirements

Applicant must have a bachelor's degree with at least 5+ years of experience; OR a master's degree with at least 3+ 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 building or supporting data pipelines, integrations, APIs, data models, SQL queries, dashboards, or analytics-ready datasets.
Experience working with manufacturing, supply chain, quality, laboratory, ERP, MES, historian, or operational data sources.
Strong problem-solving, documentation, communication, and stakeholder engagement skills.
Permanent work authorization in the United States.

Nice To Haves

Experience with SAP, MES, LIMS, CMMS, historians, Snowflake, SQL, Python, Power BI/Tableau, APIs, cloud data platforms, or data orchestration tools preferred.
Experience in pharmaceutical, GMP, biologics, API, sterile manufacturing, or another regulated environment preferred.
Knowledge of data governance, data integrity, cybersecurity, validation, metadata, lineage, and responsible AI data practices preferred.

Responsibilities

Serve as a Front Door strategic partner to assigned site organizations for data enablement; understand each area's process flow and data sources firsthand.
Assess proposed AI, analytics, and automation use cases to determine whether required data is sufficiently available, reliable, accessible, appropriately governed, and fit for purpose before technical build begins.
Design, build, and maintain data pipelines and integrations across Core and Local Systems, sensors, Power BI/Tableau, quality systems, and other approved operational data sources.
Create clean, reusable, well-documented data models and data assets that accelerate AI, analytics, automation, and reporting use cases.
Build or support APIs, system integrations, data transformations, and data access patterns aligned with enterprise standards.
Partner with business process owners and data owners to define requirements for data availability, latency, quality, lineage, access, support, and long-term ownership.
Implement data quality checks, issue resolution processes, and monitoring to improve reliability and reduce manual data correction or reconciliation work.
Support AI model development and deployment by preparing analytical datasets, features, source-system mappings, and production-ready data flows.
Maintain documentation for data sources, data definitions, transformation logic, dependencies, controls, and handoffs to support teams.
Contribute to reusable site data architecture patterns that improve speed to value and reduce one-off solutions.