> Markdown version of [/jobs/ext/4202081-data-modeller](https://www.wearedevelopers.com/jobs/ext/4202081-data-modeller). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Data Modeller

- **Company:** The Nam
- **Location:** Columbus, OH, United States
- **Experience:** Expert
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Data Architecture, Data Validation, Data Governance, Data Integrity, Meta-Data Management, Online Analytical Processing, Machine Learning Operations, Physical Data Models, Software Version Control, Databricks
- **Published:** October 17, 2026
- **Apply:** https://www.careerjet.com/jobad/usacbec5b8ec9ac29015b0ca23cc81f719

## About the Role

* 8 10 years of experience in data modeling and data architecture.
 * Strong understanding of conceptual, logical, and physical data modeling.
 * Hands-on experience with ERwin Data Modeler.
 * Good understanding of Databricks concepts and data modeling practices.
 * Experience designing fact tables, dimension tables, and data relationships.
 * Strong knowledge of primary keys, foreign keys, cardinality, and referential integrity.
 * Experience with data standards, metadata management, and data documentation.
 * Understanding of data quality validation and model performance optimization.
 * Ability to design scalable and reliable data models for analytical and AI/BI workloads.
 * Strong analytical, problem-solving, and communication skills.

## Description

* Design and maintain conceptual, logical, and physical data models.
 * Define entities, relationships, primary keys, and foreign keys, and document relationship cardinalities.
 * Specify the grain and row-level meaning of fact, event, and feature tables.
 * Design dimension tables with consistent codes, descriptions, and hierarchy attributes.
 * Create fact and event tables with appropriate measures, timestamps, and join keys to related dimensions and entities.
 * Implement standard date and time fields, including effective start and end dates for historical data tracking where required.
 * Establish and maintain naming conventions, data types, units of measurement, and allowed values across data models.
 * Maintain metadata and documentation, including field definitions, source systems, refresh frequency, and data ownership.
 * Implement data validation checks for nullability, uniqueness, referential integrity, and value ranges.
 * Design data models to support common access patterns, including joins, filters, and aggregations for OLAP, BI, and AI pipelines.
 * Maintain model versioning and change history.
 * Assess schema changes and update downstream dependencies accordingly.
 * Optimize data models to improve performance for AI and BI use cases.