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

Banking experience is mandatory.

Key Skills

Education

Any Graduate