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# Data Modeler

- **Company:** Experis
- **Location:** Charlotte, NC, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Systems, Python (Programming Language), Large Language Models
- **Published:** September 25, 2026
- **Apply:** https://www.experis.com/en/job/412413/data-modeler

## About the Role

Our client is seeking a Data Modeler to join their team. As a Data Modeler, you will be part of a growing Data & Analytics organization focused on bridging the gap between business needs and technical data solutions. The ideal candidate will possess strong data modeling expertise, a consultative mindset, and the ability to collaborate effectively across both technical and business teams., + Strong experience with Data Modeling and Data Warehousing concepts

 + Deep understanding of fact tables, dimension tables, star schemas, data grain, cardinality, surrogate keys, and Type 2 Slowly Changing Dimensions (SCDs)
 + Strong SQL skills with the ability to understand data structures, relationships, and joins
 + Experience with Snowflake and modern cloud-based data environments
 + Experience with data profiling and data quality analysis
 + Ability to communicate effectively with both technical and non-technical stakeholders
 + Consultative mindset with strong problem-solving and facilitation skills
 + Experience with DBT, Python, AI/LLM tools, ERP systems, or business domains such as Sales, Supply Chain, Manufacturing, or Commercial Operations is a plus

## Description

+ Design scalable enterprise data models that support reporting, analytics, and business decision-making
 + Partner with Data Engineers, Business Analysts, and Data Quality teams to translate business requirements into effective data structures
 + Define and refine fact tables, dimension tables, star schemas, and other dimensional modeling solutions
 + Perform data profiling and analyze source data to understand relationships, data quality concerns, and business context
 + Facilitate discussions between technical and business teams to drive clarity, challenge assumptions, and improve data design outcomes
What's Needed?


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