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
Job source
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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