Data Modeller

Apptad Inc; Jersey City (NJ); On-site; USD 90,000 - 130,000; Full time

Job summary

Apptad Inc. seeks a data modeling specialist to define entities, relationships, and keys for robust data schemas. You will design dimensions and facts, ensure consistent grain, and establish time-based history with valid metadata. The role focuses on OLAP/AI readiness, with emphasis on performance optimization and schema versioning.

Experience with Databricks and Erwin is desirable; you will document domains, validation rules, and data ownership, supporting reliable analytics pipelines.

Qualifications

• Define entities, relationships, and keys; document cardinalities.
• Specify table grain (row-level meaning) for each fact/event.
• Create dimension tables with consistent codes and hierarchies.
• Create fact tables with measures, timestamps, and keys.
• Represent time with standard date/time fields; history windows.
• Standardize naming conventions, data types, and domains.
• Add metadata, documentation, and ownership details.
• Implement basic validation: nullability, RI, ranges.
• Design outputs for OLAP/AI use cases and downstream schemas.
• Maintain versioning/history and downstream impact awareness.
• Optimize models for AI/BI performance; leverage Databricks.

Skills

Define entities and relationships

Document cardinalities

Table grain specification

Dimension table design

Fact/table design with measures

Time-based data modeling

Naming conventions and domains

Metadata and documentation

Validation checks (nullability, RI)

OLAP/AI pipeline design

Databricks concepts

Tools

Erwin

Job description

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