Company Description Define entities, relationships, primary keys, and foreign keys, and document cardinalities.
Specify the table grain (row-level meaning) for each fact, event, and feature table.
Create dimension tables with consistent codes, descriptions, and hierarchy attributes where applicable.
Create fact and event tables with measures, timestamps, and join keys to relevant dimensions and entities.
Represent time using standard date and time fields, and implement effective start and end dates when historical tracking is required.
Standardize naming conventions, data types, units of measure, and allowed values across data models.
Add metadata and documentation, including field definitions, source systems, refresh cadence, and data ownership information.
Implement basic data validation checks, including nullability, uniqueness, referential integrity, and value range validations.
Design data models and outputs optimized for common access patterns such as joins, filters, and aggregations used in OLAP and AI pipelines.
Maintain version control and change history for data models.
Update downstream dependencies when schema changes occur.
Optimize data models for AI, analytics, and BI performance requirements.
Work closely with data engineers, data architects, and business stakeholders to support data integration, transformation, and loading processes in cloud environments.
Demonstrate a strong understanding of Databricks concepts and architecture.
Possess good working knowledge of data modeling tools such as Erwin.
Have 7 to 10 years of relevant experience in data modeling and data warehousing.

Lead Data Modeler

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