Data Modeler

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

Data Modeler with Erwin Experience

Job Description: client is looking for strong Erwin tool experience.

Role Description

• Define entities, relationships, and keys (primary/foreign keys) and document cardinalities
• Specify table grain (row-level meaning) for each fact/event/feature table
• Create dimension tables with consistent codes, descriptions, and hierarchy attributes where applicable
• Create fact/event tables with measures, timestamps, and join keys to relevant dimensions/entities
• Represent time using standard date/time fields and, when needed, effective start/end dates for history
• Standardize naming conventions, data types, units, and allowed values (domains) across models
• Add metadata and documentation (field definitions, source system, refresh cadence, owner)
• Implement basic validation checks (nullability, uniqueness, referential integrity, value ranges)
• Design outputs for common access patterns (joins, filters, aggregations) used by OLAP and AI pipelines
• Maintain versioning/change history for models and update downstream dependencies when schemas change
• Optimize the models for performance of AI/BI use cases
• Good understanding of Databricks concepts
• Strong work experience of tools like Erwin