Data Modeler

Role: Data Modeler  

Location: Remote

We are looking for Finance Data modeler who has worked on Databricks as well with 10+ years of experience.  

We are seeking an experienced Data Modeler to design, develop, and maintain scalable enterprise data models that support analytics, reporting, operational applications, and data integration initiatives.

The Data Modeler will work closely with business stakeholders, data architects, data engineers, analysts, and application teams to translate business requirements into clear conceptual, logical, and physical data models. This role will help establish consistent data definitions, relationships, standards, and governance practices across the organization.

The ideal candidate has strong experience with enterprise data modeling, relational and dimensional modeling, data warehousing, cloud data platforms, and modern data architecture practices.

Key Responsibilities

• Design and maintain conceptual, logical, and physical data models for enterprise data platforms and business applications.
• Translate business requirements and business processes into scalable and reusable data structures.
• Develop relational, dimensional, star-schema, snowflake-schema, and domain-oriented data models as appropriate.
• Define entities, attributes, relationships, primary keys, foreign keys, business keys, hierarchies, and reference-data structures.
• Define and enforce naming conventions, modeling standards, data types, and modeling best practices.
• Partner with business and technical teams to establish consistent definitions for critical business data.
• Support master data, reference data, metadata, and data-governance initiatives.
• Maintain model documentation, business definitions, lineage, and metadata in enterprise modeling or cataloging tools.
• Participate in design reviews and ensure proposed database and analytics solutions comply with established architecture standards.
• Experience with data-modeling tools such as ER/Studio, ERwin, PowerDesigner, Visio, Lucidchart, or equivalent platforms.
• Ensure models appropriately support historical data, slowly changing dimensions, auditability, and data traceability where required.
• Contribute to data-quality controls by identifying required fields, valid relationships, integrity constraints, and business rules.