Job Title: Senior Salesforce AI, Data Cloud & Analytics Architect
Location: Coral Springs, Florida OR Alpharetta, GA OR Berkeley Heights, New Jersey OR Frisco, Texas OR Brookfield, Wisconsin
(It’s hybrid for local candidates and remote for non-locals but occasionally they have to travel to any of the client locations.)
Senior Salesforce AI, Data Cloud & Analytics Architect
Role Summary
We are seeking a Senior Salesforce AI, Data Cloud & Analytics Architect to serve as a hands-on enterprise Subject Matter Expert (SME) for Salesforce Agentforce, Data Cloud (Data 360), and Tableau Next. This role partners closely with Solution Architects, Sales, Service, Marketing, Data Platform, Security, and Business teams to design and deliver AI-powered, data-driven capabilities across the Salesforce ecosystem.
This individual is a hands-on technical and strategic contributor responsible for defining platform architecture, agent design patterns, data model strategy, semantic layer standards, integration approaches, governance practices, and analytics enablement for enterprise customer intelligence and AI-driven business processes.
Scope, Accountability & Impact (Senior Level)
Typically 10+ years of experience in enterprise application architecture, CRM platforms, data platforms, analytics, AI-enabled solutions, or related technology domains.
5+ years of Salesforce architecture experience supporting enterprise-scale Sales, Service, Marketing, Data Cloud, analytics, or platform implementation initiatives.
Serves as the primary SME for Agentforce, Data Cloud (Data 360), and Tableau Next architecture, implementation patterns, governance, and adoption strategy.
Defines reusable architecture standards, reference designs, platform guardrails, and technical roadmaps for Salesforce AI, data, and analytics capabilities.
Identifies and escalates platform, security, data quality, compliance, integration, scalability, and operational risks to Solution Architects, platform leadership, and enterprise architecture teams.
Influences enterprise strategy related to AI adoption, customer intelligence, data activation, analytics modernization, and Salesforce platform consolidation.
Key Responsibilities
Agentforce Strategy & AI Solution Architecture
Serve as the enterprise SME for Salesforce Agentforce capabilities, AI agent design, and AI-enabled business processes.
Design and support Agentforce agents, agent actions, orchestration patterns, prompts, grounding strategies, and enterprise use cases.
Partner with business stakeholders to identify, assess, prioritize, and implement AI automation opportunities across Sales, Service, Marketing, and Operations.
Define Responsible AI practices, security guardrails, approval patterns, escalation rules, testing approaches, and operational controls for production AI agents.
Translate business objectives into practical Agentforce solution designs that are secure, scalable, supportable, and aligned with enterprise architecture standards.
Data Cloud (Data 360) Architecture & Customer Data
Strategy
Design and govern Salesforce Data Cloud architecture supporting customer 360, identity resolution, segmentation, activation, analytics, and AI use cases.
Define enterprise customer data models, canonical entities, data mapping standards, harmonization approaches, and stewardship practices.
Architect ingestion and integration of data from Salesforce, Snowflake, marketing platforms, service systems, ERP platforms, APIs, and other enterprise sources.
Support batch, near real-time, and event-driven data patterns required to enable Agentforce, Tableau Next, personalization, analytics, and operational workflows.
Establish data quality, lineage, certification, access control, privacy, and governance expectations for Salesforce Data Cloud data products.
Tableau Next & Analytics Enablement
Serve as a technical SME for Tableau Next and Salesforce analytics capabilities used to deliver governed enterprise reporting, insights, and AI-assisted analytics.
Design semantic models, reusable business metrics, KPI definitions, dimensions, measures, and analytic frameworks that support consistent enterprise decision-making.
Partner with business leaders and analysts to deliver executive dashboards, operational reporting, self-service analytics, and natural language insight experiences.
Define best practices for data visualization, semantic governance, dashboard performance, certified assets, user enablement, and analytics lifecycle management.
Ensure Tableau Next solutions align with enterprise data governance standards and integrate appropriately with Salesforce Data Cloud, Snowflake, and other trusted data sources.
Enterprise Integration & Platform Architecture
Define integration patterns connecting Salesforce, Data Cloud, Snowflake, APIs, middleware, cloud platforms, and enterprise systems.
Collaborate with engineering teams to design secure, scalable, maintainable, and observable platform solutions.
Evaluate emerging Salesforce AI, Agentforce, Data Cloud, Tableau Next, and platform capabilities and recommend practical adoption strategies.
Create reference architectures, solution designs, standards, decision records, technical roadmaps, and implementation guidance for delivery teams.
Support modernization efforts involving CRM consolidation, customer data unification, analytics rationalization, and AI-enabled workflow automation.
Technical Leadership & Platform Governance
Provide technical leadership, mentoring, and guidance to architects, developers, administrators, analysts, and implementation partners.
Lead design workshops, architecture reviews, proof-of-concepts, demos, technical assessments, and executive-level solution walkthroughs.
Establish governance frameworks for platform scalability, compliance, security, data access, model quality, AI behavior, and operational readiness.
Partner with Salesforce account teams, implementation partners, enterprise architecture, information security, and business stakeholders to drive successful outcomes.
Champion adoption of Salesforce AI, data, and analytics capabilities while ensuring solutions remain practical, secure, governed, and aligned to enterprise priorities.
Required Qualifications
Direct hands-on experience with Salesforce Agentforce, Einstein AI, Salesforce AI capabilities, or enterprise AI-enabled platform solutions.
Direct hands-on experience with Salesforce Data Cloud / Data 360, including data modeling, ingestion, identity resolution, segmentation, activation, or governance.
Experience with Tableau Next, Tableau, or equivalent enterprise analytics and semantic modeling platforms.
Strong Salesforce platform architecture experience across Sales Cloud, Service Cloud, Platform, integrations, security model, metadata, automation, and data architecture.
Experience designing customer 360 solutions, semantic data models, governed data products, and enterprise reporting or analytics frameworks.
Strong understanding of APIs, event-driven architecture, data integration patterns, middleware, cloud platforms, and enterprise data platforms such as Snowflake.
Experience defining architecture standards, solution designs, technical roadmaps, governance models, and implementation patterns for enterprise platforms.
Strong knowledge of data governance, privacy, compliance, access controls, auditability, and security expectations in regulated enterprise environments.
Excellent executive communication, stakeholder management, technical translation, documentation, and cross-functional leadership skills.
Preferred / Strong Plus Qualifications
Salesforce Certified Technical Architect (CTA) or Salesforce Architect-level certifications.
Salesforce Data Cloud Consultant Certification.
Salesforce AI Specialist, AI Associate, or related Salesforce AI certifications.
Tableau certifications or hands-on experience delivering enterprise Tableau analytics solutions.
Experience with Snowflake, Snowflake Cortex, semantic layers, AI/ML platforms, or enterprise data lakehouse / medallion architecture patterns.
Experience integrating Salesforce with Snowflake, AWS, Azure, Google Cloud, middleware, API gateways, or enterprise data platforms.
Experience leading enterprise AI governance, Responsible AI adoption, platform modernization, or customer intelligence initiatives.
Experience within financial services, payments, fintech, or other highly regulated industries.
Experience working in Agile delivery models with product owners, architects, engineering teams, QA, security, and operations teams.
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