Data Modeller

The Nam
Columbus, OH, United States
23 days ago
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Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours

Tech stack

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

Job 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.

Requirements

  • 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.

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