Senior Data Modeler / Data ArchitectThis 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.Position OverviewWe 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 ResponsibilitiesOwn the structure and organization of data across an enterprise data platform modernization initiativeDefine conceptual, logical, and physical data models for enterprise data domainsConsolidate scattered business analysis artifacts and requirements into a single coherent data modelDetermine which data objects belong together, how they relate, and how they should be organizedDesign scalable dimensional models to support reporting, analytics, and business use casesEstablish the blueprint for how data is organized across schemasDefine where data is ingested, how it moves, how it is transformed, and how it joins across the ecosystemPartner with business stakeholders, business systems analysts, data engineers, architects, and system ownersDocument data sources, lineage, relationships, business definitions, and modeling decisionsEvaluate new requirements, source systems, third-party integrations, and business reporting needsDetermine how new data requirements fit into and extend the existing data modelProvide feasibility analysis and solution options with clear pros, cons, tradeoffs, and recommendationsCommunicate modeling decisions clearly, including what is being built, why it matters, and how it scalesRequired QualificationsSenior-level data modeling or data architecture experienceProven experience building, scaling, and evolving enterprise data models over timeDeep dimensional modeling expertise, including facts, dimensions, star schemas, conformed dimensions, and data martsStrong experience creating conceptual, logical, and physical data modelsStrong understanding of how business users, reporting teams, analytics teams, and data engineers use data objectsAbility to convert vague, incomplete, or scattered requirements into a clear and scalable data modelExperience documenting data sources, lineage, definitions, relationships, and modeling logicAbility to explain modeling decisions, tradeoffs, scalability considerations, and business impactStrong communication skills with the ability to work across technical and non-technical teamsAbility to operate in an ambiguous, fast-moving modernization environmentPreferred QualificationsMortgage, lending, loan origination, servicing, capital markets, banking, financial services, or highly regulated industry experienceUnderstanding of how enterprise datasets support reporting, revenue, marketing, operations, and business decision-makingExperience designing models for enterprise reporting and business enablement at scaleExperience with data platform modernization, data warehouse redesign, cloud data platforms, or data product initiativesExperience with tools such as Erwin, Lucidchart, ER/Studio, dbt documentation, or similar modeling toolsExperience with modern data platforms such as Databricks, Snowflake, Azure, or similar environmentsIdeal CandidateTrue senior data modeler / architect, not just a diagramming resourceAble to explain what they are building, why it matters, and how it supports the businessStrong judgment around how data should be structured and scaled over timeComfortable challenging assumptions and validating whether a solution actually fits the environmentHas built and evolved data models across multiple organizations or complex enterprise environmentsStrong enough technically to earn trust from data engineers and architectsBusiness-facing enough to work with stakeholders who may not fully know what they need