CloudIngest

Data Modeler

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is a Data Modeler Engineer based in Atlanta, GA / Charlotte, NC / Raleigh, NC, with a contract length of 6-12 months at $44/hr on W2. Key skills include Data Modeling, Snowflake, advanced SQL, SAS, ETL concepts, and Data Warehousing.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
352
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🗓️ - Date
May 27, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Atlanta, GA
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🧠 - Skills detailed
#Vault #Data Pipeline #Data Warehouse #SAS #Data Integrity #Data Modeling #BI (Business Intelligence) #"ETL (Extract #Transform #Load)" #Metadata #SQL (Structured Query Language) #Clustering #Snowflake #Scala #Data Management #Data Vault #Data Analysis
Role description
Role: Data Modeler Engineer Location: Atlanta, GA / Charlotte, NC / Raleigh, NC (On-Site) Duration: 6-12 months with multiple extensions Rate: $44/hr on W2 (NO C2C) Required Skills • Data Modeling • Snowflake • SQL (Advanced) • SAS (data analysis understanding) • ETL Concepts • Data Warehousing • ER Modeling Job Description • Design and implement enterprise-grade data models (logical & physical), define standards, support Snowflake architecture, and optimize data structures for performance and scalability. • Conduct impact analysis and ensure model alignment with business needs. • Engineered scalable ER models (Conceptual, Logical, Physical) for enterprise-grade data platforms. • Designed high-performance Data Warehouse schemas (Star & Snowflake) for analytics optimization. • Specialized in Dimensional Modeling (Kimball) and Data Vault 2.0 architecture. • Developed optimized Fact & Dimension tables to support large-scale reporting and BI workloads. • Leveraged Snowflake expertise for data modeling, clustering, and performance tuning. • Utilized advanced SQL for complex transformations, validations, and query optimization. • Partnered with ETL teams to architect efficient, scalable data pipelines. • Ensured data integrity, governance, quality, and robust metadata management. • Translated complex business requirements into scalable, future-ready data models. • Built end-to-end Data Warehouse architecture (Staging → Core → Presentation/BI layers).