Data Specialist

Caterpillar Inc.•Mossville, IL

1d•$89,210 - $133,810•Onsite

About The Position

Caterpillar's SPPD Digital team is seeking a Data Specialist to support Procurement and Supply Chain decision-making through modern cloud-based data engineering and analytics solutions. This role sits within the Data Engineering team and supports the foundational data platforms that power digital products, reporting, analytics, and decision-making across SPPD. In this role, you will design, build, maintain, and troubleshoot analytical data products and data pipelines using Snowflake, SQL, Python, and modern development tools while helping improve data quality, consistency, and governance across multiple platforms. This position combines hands-on data engineering, data investigation, production support, and stakeholder engagement, requiring an individual who can solve complex problems and communicate technical concepts effectively to both technical and business audiences. This is more than a traditional data specialist role. You'll work at the foundation of SPPD Digital's data ecosystem, helping create trusted, standardized data that supports critical Procurement and Supply Chain decisions across the enterprise. No two days are exactly alike. One day you may be building and modernizing data pipelines in Snowflake, and the next you may be investigating a complex data issue, partnering with business stakeholders to identify the root cause, and helping improve data quality and consistency across multiple systems. You'll have the opportunity to support cloud modernization initiatives, work with emerging technologies and AI-enabled tools, and learn from experienced data engineers and platform specialists while making a visible impact across the broader organization.

Requirements

Ability to communicate technical concepts, data definitions, root-cause findings, and solution recommendations in a clear and understandable manner for both technical and non-technical audiences.
Experience translating complex data issues into actionable business insights and helping stakeholders understand impacts, risks, and resolutions.
Comfortable operating at the intersection of technology and business while building strong partnerships across functions.
Working knowledge of Agile methodologies and software development practices.
Experience supporting iterative development, prioritizing changing business needs, and delivering value in a fast-paced environment.
Familiarity with version control, collaborative development workflows, testing practices, and tools such as GitHub, VS Code, and GitHub Copilot.
Experience building, supporting, and maintaining data pipelines that acquire, transform, validate, and deliver data from multiple business systems.
Ability to troubleshoot data integration issues, investigate discrepancies, and ensure data accuracy throughout the data lifecycle.
Understanding of data quality controls, transformation logic, lineage, and modern cloud-based data solutions.
Working knowledge of cloud-based data platforms and data solutions, including data migration, integration, deployment, security, and troubleshooting activities.
Experience working with Snowflake or similar cloud data warehouse technologies.
Familiarity with modern AI-enabled tools and cloud-based data engineering environments.
Ability to collect, organize, manage, and analyze information from multiple sources while supporting data quality, governance, and information-management practices.
Experience developing trusted, well-documented analytical data products that support business decision-making.
Understanding of data stewardship, business definitions, and enterprise information management concepts.
Strong SQL skills and working knowledge of relational databases, database structures, and physical database design concepts.
Ability to analyze relationships between business data sets, validate data structures, and support solutions that drive effective business outcomes.
Experience writing, debugging, optimizing, and validating complex queries across multiple data sources.
Understanding of data models, logical relationships, transformations, normalization, and entity relationships.
Ability to analyze how data moves across systems and support documentation of data flows, definitions, and lineage.
Experience identifying opportunities to improve data consistency, quality, and usability across business processes.
Basic understanding of how data solutions support business objectives, operational efficiency, and decision-making outcomes.
Ability to understand the connection between technical data work and the business processes it supports across Procurement and Supply Chain functions.
May contribute to initiatives that improve data quality, consistency, reporting accuracy, or business processes under the guidance of senior team members.
Supports the delivery of trusted data products and analytical solutions that enable business stakeholders to make informed decisions.

Nice To Haves

Bachelor's degree in Computer Science, Information Systems, Engineering, Data Analytics, Data Science, or a related discipline; equivalent experience demonstrating strong technical capability and measurable results will also be considered.
Experience in Data Engineering, Analytics Engineering, Business Intelligence, Data Analytics, Data Science, or a related technical data role.
Demonstrated success delivering analytical data solutions, maintaining data products, and solving complex data, reporting, or integration issues.
Experience working with Snowflake, SQL, cloud-based data platforms, modern data engineering practices, and analytical data pipelines.
Experience modernizing legacy data solutions, including SAS-based processes, into scalable cloud-based architectures.
Experience supporting Procurement, Supply Chain, Demand Planning, Inventory Management, Logistics, Operations Analytics, or similar business functions.
Experience with Power BI, Tableau, dbt, semantic data layers, enterprise data architecture, data governance, data quality initiatives, or master data management practices.
Familiarity with AI-enabled analytics, intelligent automation, agentic AI concepts, or modern development tools such as GitHub Copilot.
Strong communication, collaboration, stakeholder management, and technical documentation skills.
Ability to independently investigate complex data challenges, trace information across systems and reporting layers, determine root causes, and clearly communicate findings and recommendations.
Ability to operate comfortably between technical teams and business stakeholders while translating complex concepts into clear, actionable information.
Experience supporting trusted data definitions, improving data quality, and contributing to cross-functional data standards and governance efforts.
Adaptability to changing priorities, balancing project work, troubleshooting, production support, and business requests in a fast-paced environment.
Curiosity, attention to detail, and a proactive approach to solving challenging data problems while contributing to a collaborative team environment.

Responsibilities

Design, build, maintain, and support data pipelines, data transformations, and analytical data products using Snowflake, SQL, and modern development tools.
Support the modernization of legacy SAS-based processes into scalable Snowflake-based data solutions, including rebuilding, validating, and retiring legacy processes where appropriate.
Develop reliable, repeatable, and well-documented data products that support Procurement and Supply Chain decision-making.
Follow established coding standards, version control practices, and development workflows utilizing tools such as GitHub, VS Code, and GitHub Copilot.
Contribute to cloud-based data architecture initiatives that improve data accessibility, maintainability, and performance.
Investigate data discrepancies, reporting inconsistencies, and production issues by tracing data through source systems, transformation logic, and downstream applications.
Identify root causes related to data quality, mapping, lineage, source-system changes, and analytical reporting.
Resolve production support issues across analytical applications and backend data products, escalating issues appropriately when resolution requires upstream teams or platform owners.
Analyze large datasets to identify accuracy concerns, recommend corrective actions, and improve overall data reliability.
Balance project-based work with changing priorities, urgent business requests, troubleshooting activities, and production support needs.
Support efforts to establish trusted, standardized data definitions across Procurement and Supply Chain systems and platforms.
Help improve and maintain data quality standards, governance practices, and approved-source alignment across the SPPD Digital landscape.
Partner with Data Platform teams to support consistent data flows, business definitions, and enterprise data quality initiatives.
Partner with Product Owners, Business Process Owners, Data Stewards, and Analytics teams to translate business needs into scalable technical solutions.
Communicate technical concepts, issue-resolution details, and data impacts clearly to both technical and non-technical stakeholders.
Develop practical documentation describing data definitions, transformations, technical specifications, and lineage, while participating in Agile development, team reviews, and continuous improvement initiatives.