Lead Data Modeler

Saransh Inc; Columbus (OH); On-site; USD 110,000 - 150,000; Full time

Job summary

Saransh Inc in Columbus, OH is seeking a senior Data Modeler to design scalable data models and support AI/BI pipelines. You will define entities, relationships, keys, and documentation for warehouse solutions and metadata.

The ideal candidate has 7–10 years of experience in data modeling and data warehousing, with Databricks concepts and ERWin proficiency, collaborating closely with data engineers and business stakeholders.

Qualifications

• 7–10 years of relevant experience in data modeling and data warehousing.
• Strong knowledge of Databricks concepts and architecture.
• Experience creating dimension and fact tables and metadata documentation.
• Ability to work with data engineers, data architects, and business stakeholders.

Responsibilities

• Define entities, relationships, primary keys, and foreign keys; document cardinalities.
• Create dimension and fact tables with measures, timestamps, and join keys.
• Standardize naming conventions, data types, units, and allowed values across models.
• Collaborate with data engineers and business stakeholders to support data integration and BI pipelines.

Skills

Databricks

Data modeling

Data warehousing

Stakeholder collaboration

Erwin

Tools

Erwin

Job description

• Company Description Define entities, relationships, primary keys, and foreign keys, and document cardinalities.
• Specify the table grain (row-level meaning) for each fact, event, and feature table.
• Create dimension tables with consistent codes, descriptions, and hierarchy attributes where applicable.
• Create fact and event tables with measures, timestamps, and join keys to relevant dimensions and entities.
• Represent time using standard date and time fields, and implement effective start and end dates when historical tracking is required.
• Standardize naming conventions, data types, units of measure, and allowed values across data models.
• Add metadata and documentation, including field definitions, source systems, refresh cadence, and data ownership information.
• Implement basic data validation checks, including nullability, uniqueness, referential integrity, and value range validations.
• Design data models and outputs optimized for common access patterns such as joins, filters, and aggregations used in OLAP and AI pipelines.
• Maintain version control and change history for data models.
• Update downstream dependencies when schema changes occur.
• Optimize data models for AI, analytics, and BI performance requirements.
• Work closely with data engineers, data architects, and business stakeholders to support data integration, transformation, and loading processes in cloud environments.
• Demonstrate a strong understanding of Databricks concepts and architecture.
• Possess good working knowledge of data modeling tools such as Erwin.
• Have 7 to 10 years of relevant experience in data modeling and data warehousing.