Data Modeler 360 + Lake

The Data Modeler will own the canonical data model across Salesforce Data 360 and an enterprise data lake, ensuring that both platforms use consistent definitions for critical entities such as accounts, partners, assets, cases, leads, and claims. This person will design and implement models across Data 360 DMOs and bronze, silver, and gold lake layers; map information from CRM, marketing, ERP, and manufacturing systems; establish identity-resolution rules; and govern lineage and sensitive-data classifications. The ideal candidate combines hands-on CDP and lakehouse modeling experience with strong data governance, identity resolution, and AI-enabled delivery skills. * Own the canonical definition and structure of critical enterprise entities across Data 360 and the data lake * Design and deploy unified models for accounts, partners, assets, cases, leads, claims, and related entities * Implement consistent schemas across Data 360 DMOs and bronze, silver, and gold lake layers * Translate agreed entity definitions into lake objects and KPI-ready data grains * Map data flows from SAP, MES, Marketing Cloud, and CRM through the data lake and into Data 360 * Define identity-resolution, matching, reconciliation, and unified-profile rules * Resolve conflicts between source-system definitions and establish the approved canonical model * Document source-to-target mappings, data lineage, metadata, and PII classifications * Partner with data engineering teams to ensure pipelines correctly implement the approved model * Validate production data publishes and formally sign off on model readiness * Support downstream analytics, segmentation, activation, and customer or partner insights * Design models that improve Agentforce retrieval quality, not solely reporting and dashboard performance * Use AI copilots and agents to accelerate field-mapping proposals, lineage drafts, model documentation, and quality checks * Review and validate AI-generated outputs before implementing production decisions * Maintain model consistency as source systems and business requirements evolve

• 5+ years of hands-on data modeling experience within CDP, lakehouse, enterprise CRM, or similarly complex data environments
• Experience designing canonical, conceptual, logical, and physical data models
• Hands-on Salesforce Data Cloud/Data 360 experience or directly comparable CDP experience
• Hands-on lake or lakehouse schema-design experience
• Experience modeling data across bronze, silver, and gold layers
• Strong identity resolution, entity matching, data harmonization, and unified-profile experience
• Experience modeling core business entities such as Account, Partner, Asset, Case, Lead, or Claim
• Experience mapping data from multiple enterprise source systems into unified models
• Strong data lineage, metadata management, PII classification, and data-governance experience
• Ability to implement models rather than only create conceptual designs or documentation
• Experience partnering with data engineers, architects, product owners, and data-governance teams
• Ability to use AI-assisted tooling for mapping, discovery, lineage, and documentation while independently validating production decisions
• Salesforce Data Cloud Consultant certification
• Deep experience with Data Model Objects, standard DMOs, and custom DMOs
• Experience with Salesforce CRM data structures
• Experience integrating or modeling SAP, MES, Marketing Cloud, and CRM data
• Strong SQL skills for model validation and data-quality analysis
• Experience with dimensional modeling and KPI-ready analytical grains
• Knowledge of Data Vault, star-schema, snowflake-schema, or domain-driven modeling approaches
• Experience supporting customer, partner, dealer, distributor, or channel data
• Experience modeling data for manufacturing, HVAC, industrial, or field-service businesses
• Familiarity with Agentforce, retrieval-augmented generation, or AI retrieval use cases
• Experience designing data models that support AI retrieval quality as well as traditional reporting
• Familiarity with data catalogs, lineage platforms, or governance tools Experience supporting real-time or high-volume customer data platforms