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
• Good understanding of tools like Erwin

Key Skills

Education

Not specified