Job Summary:
Scalence L.L.C. is seeking a Data Modeler Engineer to design and implement enterprise-grade data models. The role involves optimizing data structures for performance and scalability while ensuring alignment with business needs.
Responsibilities:
• 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)
Qualifications:
Required:
• 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)
Company:
In today’s dynamic and competitive market, success hinges on mastering three key areas: Data Intelligence, Business Resilience, and Digital Experience. Founded in , the company is headquartered in Morristown, USA, with a team of 501-1000 employees. The company is currently Late Stage.