Sr. Data Modeler / Architect

This role requires candidates who are currently authorized to work in the U.S. without sponsorship, and C2C arrangements are not accepted. This role is onsite near Tustin, CA.

Please submit your resume and send to recruiters@vsearchgroup.com if you are interested. Thank you!

Position Overview

We are seeking a Senior Data Modeler / Data Architect to support an enterprise data platform modernization initiative. This role will own how enterprise data is structured, organized, related, and scaled across the platform.

The ideal candidate is a seasoned, hands-on data modeler and architect with deep dimensional modeling expertise. This person must be able to take scattered requirements, business analysis artifacts, source-system knowledge, and stakeholder needs and turn them into a coherent, scalable data model that supports reporting, analytics, and business enablement.

Key Responsibilities

• Own the structure and organization of data across an enterprise data platform modernization initiative
• Define conceptual, logical, and physical data models for enterprise data domains
• Consolidate scattered business analysis artifacts and requirements into a single coherent data model
• Determine which data objects belong together, how they relate, and how they should be organized
• Design scalable dimensional models to support reporting, analytics, and business use cases
• Establish the blueprint for how data is organized across schemas
• Define where data is ingested, how it moves, how it is transformed, and how it joins across the ecosystem
• Partner with business stakeholders, business systems analysts, data engineers, architects, and system owners
• Document data sources, lineage, relationships, business definitions, and modeling decisions
• Evaluate new requirements, source systems, third-party integrations, and business reporting needs
• Determine how new data requirements fit into and extend the existing data model
• Provide feasibility analysis and solution options with clear pros, cons, tradeoffs, and recommendations
• Communicate modeling decisions clearly, including what is being built, why it matters, and how it scales

Required Qualifications

• Senior-level data modeling or data architecture experience
• Proven experience building, scaling, and evolving enterprise data models over time
• Deep dimensional modeling expertise, including facts, dimensions, star schemas, conformed dimensions, and data marts
• Strong experience creating conceptual, logical, and physical data models
• Strong understanding of how business users, reporting teams, analytics teams, and data engineers use data objects
• Ability to convert vague, incomplete, or scattered requirements into a clear and scalable data model
• Experience documenting data sources, lineage, definitions, relationships, and modeling logic
• Ability to explain modeling decisions, tradeoffs, scalability considerations, and business impact
• Strong communication skills with the ability to work across technical and non-technical teams
• Ability to operate in an ambiguous, fast-moving modernization environment

Preferred Qualifications

• Mortgage, lending, loan origination, servicing, capital markets, banking, financial services, or highly regulated industry experience
• Understanding of how enterprise datasets support reporting, revenue, marketing, operations, and business decision-making
• Experience designing models for enterprise reporting and business enablement at scale
• Experience with data platform modernization, data warehouse redesign, cloud data platforms, or data product initiatives
• Experience with tools such as Erwin, Lucidchart, ER/Studio, dbt documentation, or similar modeling tools
• Experience with modern data platforms such as Databricks, Snowflake, Azure, or similar environments

Ideal Candidate

• True senior data modeler / architect, not just a diagramming resource
• Able to explain what they are building, why it matters, and how it supports the business
• Strong judgment around how data should be structured and scaled over time
• Comfortable challenging assumptions and validating whether a solution actually fits the environment
• Has built and evolved data models across multiple organizations or complex enterprise environments
• Strong enough technically to earn trust from data engineers and architects
• Business-facing enough to work with stakeholders who may not fully know what they need