Data Modeler with Erwin & Databricks Expertise

Talentola; Columbus (OH); On-site; USD 95,000 - 125,000; Full time

Job summary

Talentola is seeking a Data Modeler with Erwin experience in Columbus, OH. The role focuses on defining entities, relationships, keys, and table grains, and creating robust dimension and fact tables for OLAP and AI pipelines.

You will standardize naming, data types, and domains, add metadata, and implement validation checks. Strong Erwin skills and Databricks knowledge are required, with emphasis on performance optimization within data models.

Qualifications

• Experience designing entities, relationships, keys and documenting cardinalities.
• Ability to define table grain, dimensions and facts for OLAP/AI pipelines.
• Knowledge of standards for naming, data types, units and domains across models.

Responsibilities

• Define entities, relationships, and keys and document cardinalities.
• Specify table grain for fact/event tables and create dimension tables.
• Represent time with date/time fields and effective periods for history.
• Standardize naming conventions, data types, units, and domains.
• Add metadata and documentation (source system, refresh cadence, owner).
• Implement basic validation checks (nullability, uniqueness, referential integrity).
• Design outputs for common access patterns used by OLAP/AI pipelines.
• Maintain versioning and update downstream dependencies when schemas change.
• Optimize models for performance in AI/BI use cases.
• Leverage Databricks concepts and strong Erwin expertise.

Skills

Data modeling

Erwin

Databricks concepts

Tools

Erwin Data Modeler

Job description

Talentola is seeking a Data Modeler with Erwin experience in Columbus, OH. The role focuses on defining entities, relationships, keys, and table grains, and creating robust dimension and fact tables for OLAP and AI pipelines.

You will standardize naming, data types, and domains, add metadata, and implement validation checks. Strong Erwin skills and Databricks knowledge are required, with emphasis on performance optimization within data models.