Data Specialist

Caterpillar Inc.•Peoria, IL

2d•$97,530 - $146,290•Onsite

About The Position

The Strategic Procurement and Planning (SPPD) Supplier Quality team is seeking a Data Specialist who can transform complex data into actionable insights that drive quality improvement, reduce warranty exposure, and elevate supplier performance across the enterprise. In this role, you will combine business intelligence (BI), data visualization, statistical analysis, and machine learning to uncover trends, identify risks, and enable smarter, faster decision-making. You’ll partner closely with quality, procurement, and engineering teams to translate real-world manufacturing challenges into scalable analytics solutions—building dashboards, predictive models, and AI-enabled tools that deliver measurable impact. This role also supports emerging AI, automation initiatives making it an exciting opportunity to shape the future of analytics in a manufacturing environment. This is a high-visibility, high-impact role directly supporting key initiatives, including work that is influential across the enterprise. You will play an important role in managing and improving supplier warranty recovery efforts, making your contributions highly measurable and visible across the organization. The projects you’ll support are tied to critical, high-profile business priorities, offering the opportunity to see your analytics work translate into tangible financial and operational outcomes. If you are motivated by solving meaningful problems, driving enterprise impact, and working on initiatives that matter at the highest levels, this role provides a unique platform to do so.

Requirements

Value Realization: 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. Demonstrates the ability to connect data analytics, data visualization, and machine learning solutions to measurable business outcomes such as quality improvement, supplier performance optimization, and reduction of warranty exposure.
Communicating Complex Concepts: 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. Capable of translating advanced analytics, dashboards, and predictive modeling outputs into actionable insights for both technical and non-technical stakeholders, with an emphasis on clarity, impact, and decision enablement.
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. Applies modeling techniques to support analytics, structured datasets, predictive modeling, and machine learning use cases, ensuring data is organized to effectively identify trends, risks, and performance opportunities.
Information Management: Ability to collect and manage information from different sources, and distribute this information to enhance operational efficiency. Supports enterprise data initiatives by managing structured and unstructured data, enabling analytics, BI, AI, and machine learning solutions that improve visibility into supplier quality and warranty performance.
Cloud Computing: Knowledge of cloud-based solutions and ability to design, implement, and manage them. Supports analytics and machine learning workloads in cloud environments, including data storage, data pipelines, and scalable solution deployment, with the ability to troubleshoot issues and contribute to cloud-based analytics and AI initiatives.
Database Design (Physical): Develops data model for designing an organization's database that runs effectively and efficiently for better business outcomes. Participates in developing data structures that support reporting, analytics, dashboards, and machine learning datasets, ensuring performance and usability for business applications.
ETL Process: Understanding of the extraction, transformation, and loading (ETL) process; ability to develop a database through the ETL process. Ensures data accuracy and availability by acquiring, transforming, and preparing large volumes of data for use in dashboards, analytics, predictive modeling, and machine learning applications, while resolving ETL-related issues.

Nice To Haves

A background in manufacturing, supplier quality, procurement, or warranty analytics, with a clear understanding of how data enables quality improvement and supplier performance.
Demonstrated ability to develop and apply predictive analytics or machine learning models to uncover trends, identify risks, and drive actionable insights from complex datasets.
Exposure to AI-enabled tools and platforms (such as GitHub Copilot, Copilot Studio, or similar), along with an interest in leveraging automation to enhance productivity and analytics capabilities.
Hands-on experience using data science tools (e.g., Python, R, or similar languages) to support advanced analytics and modeling initiatives.
Working knowledge of data architecture and enterprise data platforms, including familiarity with data modeling practices and governance frameworks.
Proven track record of creating business intelligence dashboards and data visualizations that effectively communicate insights to both technical and non-technical audiences.
Strong consultative mindset, with the ability to bridge technical analysis and business needs, translating data insights into clear recommendations.
Success in measuring and articulating business value from analytics initiatives, such as cost savings, warranty reduction, or operational improvements.
Experience collaborating across cross-functional teams (including quality, procurement, and engineering) to deliver data-driven solutions aligned with enterprise priorities.

Responsibilities

Maintain and enhance business intelligence (BI) tools and dashboards to support supplier quality and warranty analytics.
Analyze business and customer requirements to develop scalable, implementable, and maintainable data solutions aligned to enterprise models.
Identify and resolve data quality issues across large, complex datasets from multiple enterprise sources.
Develop and apply data visualizations (dashboards, reports) to communicate insights and influence decision-making.
Build and deploy predictive analytics and machine learning models to identify quality risks, supplier performance trends, and warranty exposure.
Partner with cross-functional teams to translate business problems into statistical and machine learning approaches.
Prepare structured datasets for analytics, visualization, and machine learning through ETL processes.
Support cloud-based data environments and analytics workloads for scalable solution delivery.
Contribute to emerging AI, automation, and Copilot-enabled solutions
Work within agile development frameworks to iteratively deliver dashboards, analytics products, and AI-driven solutions.
Clearly communicate complex insights and technical concepts to both technical and non-technical stakeholders.