Data Architect (Data Models)

Required Skills & Experience

• 10+ Years Experience in Enterprise Data Modeling and Data Architecture
• 2+ Years in a Management Level role, leading data modeling and data architecture initiatives
• Architect experience in Master and Reference Data (Informatica MDM & R360)
• Hands-on experience with ErWin Data Modeler in enterprise environments and modeling approaches (relational/transactional, normalized, dimensional, and canonical (nice to have))
• Technical data analysis expertise (Databricks and PowerBI)
• Hands-on experience integrating major enterprise data platforms (ERP, CRM, OMS, MDM, AR, etc.)

Job Description

• We are seeking an Enterprise Data Modeler / Data Architect to design, govern, and evolve enterprisewide data models, master data, and reference data across a complex transactional and analytical landscape. This role owns how data is structured, integrated, and consumed across the enterprise, ensuring consistency, governance, and scalability for both operational and analytical use cases.

• The ideal candidate brings strong experience in enterprise data modeling, Master Data Management (MDM), and data architecture, and works closely with data engineering, governance, and business teams to translate complex requirements into trusted, wellgoverned data assets.

• Key Responsibilities Include:

• Own and maintain enterprise data models (conceptual, logical, and physical), including canonical, transactional, and dimensional models, while enforcing data standards, documentation, and change control

• Architect and manage Master and Reference Data using Informatica MDM and R360, including golden records, matchmerge logic, survivorship rules, stewardship workflows, and lifecycle management

• Define how transactional, master, and reference data is integrated and propagated across core enterprise systems (ERP, CRM, OMS, pricing, finance) and downstream analytics platforms

• Translate enterprise and MDM models into Databricks Lakehouse analytical schemas, partnering with data engineering teams on ELT/ETL pipelines, validation, reconciliation, and metadata lineage

• Ensure alignment with data governance, quality, security, and compliance requirements (PII, GDPR, SOX), supporting auditability from source systems through analytics

• Lead data modeling and MDM design workshops and serve as a subject matter expert, mentoring engineers and analysts on modeling and governance best practices