Senior Digital Data Steward Specialist (PIM) - Parts

Caterpillar•Chicago, IL

About The Position

Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. Cat Digital is the digital and technology arm of Caterpillar Inc., leveraging the latest technologies to build industry leading digital solutions for our customers and dealers. With over 1.5 million connected assets worldwide, our teams use data, technology, advanced analytics, telematics, and AI capabilities to help our customers build a better, more sustainable world. Senior Product Data Steward (PIM) – Parts Job Summary: The Senior Product Data Steward is responsible for the governance, quality, and lifecycle management of parts data within the Product Information Management (PIM) ecosystem. This role applies data analytics, modeling, and governance principles to ensure data is accurate, compliant, and optimized for downstream consumption across digital commerce, dealer systems, marketing, analytics, and enterprise integrations.

Requirements

Value Realization: Knowledge of value realization methods; ability to plan, execute, monitor and manage business activities and resources to determine and achieve the actual value from a business initiative as estimated in an associated business case.
Communicating Complex Concepts: Knowledge of effective presentation tools and techniques to ensure clear understanding; ability to use summarization and simplification techniques to explain complex technical concepts in simple, clear language appropriate to the audience.
Agile Development: Knowledge of agile methodologies and the agile development lifecycle; ability to utilize formal agile methodologies, disciplines, practices and techniques for the delivery of new and enhanced applications.
Cloud Computing: Knowledge of cloud-based solutions and their applications; ability to design, implement, and manage cloud-based solutions to meet business needs and improve operational efficiency.
Database Design (Physical): Knowledge of database systems; ability to establish a data model for designing an organization's database that runs effectively and efficiently for better business outcome .
ETL Process: Knowledge of the extraction, transformation and loading (ETL) process; ability to develop a database through the ETL process.
Modeling: Data, Process, Events, Objects: Knowledge of data, process and events; ability to use tools and techniques for analyzing and documenting logical relationships among data, processes or events.
Proficiency in SQL and data analysis techniques.
Ability to operate across business and technical teams.

Nice To Haves

Experience in PIM, data governance, or product data operations.
Strong understanding of enterprise data flows and integrations.
Experience with Informatica Product360, Akeneo, or similar platforms preferred.

Responsibilities

Define and enforce data quality standards, validation rules, and completeness thresholds for parts data.
Analyze data performance and integrity, identifying systemic issues and driving remediation.
Design and maintain governed data models, including attributes, hierarchies, relationships, and lifecycle states.
Serve as the accountable owner for parts data quality, governance processes, and issue resolution.
Partner with engineering and integration teams to ensure efficient, scalable, and maintainable data flows.
Establish and evolve data specifications, standards, and operating procedures across the data lifecycle.
Support audits, compliance, and data governance initiatives, ensuring adherence to enterprise information management practices.
Own parts hierarchies, fitment logic, catalog consistency, and scalability for high-volume parts data.