Data Modelling

SFE; Columbus (OH); On-site; USD 90,000 - 130,000; Full time

Job summary

SFE is seeking a data modeling expert to define entities, relationships, and keys, document grain, and create robust dimension and fact structures for analytics. The role emphasizes metadata, standard naming, data types, and domain constraints to support OLAP and AI workloads.

Responsibilities include history handling with effective dates, validation checks, versioning, and updating downstream dependencies.

Qualifications

• Define entities, relationships, and keys with documented cardinalities.
• Document grain (row-level meaning) for each fact/event table.
• Create dimension and fact tables with measures and keys.
• Standardize naming conventions, data types, domains, and units across models.
• Add metadata including source, refresh cadence, and owners.
• Implement basic validation checks (nullability, uniqueness, referential integrity, value ranges).

Skills

Data modeling

SQL

OLAP

Dimensional modeling

Metadata management

Tools

Erwin

Databricks

Job description

Must Have Technical/Functional Skills Skills / Requirements:

• 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