Technical Architect, SMB Pre-Sales

The RoleSummary
As an SMB Technical Architect, you are a pre-sales, customer-facing expert for our fastest-growing customers. You apply broad technical skills and business acumen to help sales teams align Salesforce AI, Data, and Security solutions with our customers’ data and AI transformation strategies.
Act as a subject matter expert in business strategy, application architecture, data, integration, security, governance, and AI.
Use a consultative approach to understand customer business and technical requirements
Determine optimal data/application/security/AI architectures for customers based on product knowledge, industry standards, and customer needs
Develop repeatable propositions and go-to-market strategies for sales teams
TAs in the AIforce Era
As TAs, we understand the total Salesforce architecture, from the data context layer to the foundations of Customer 360, to the new era of AI (https://www.salesforce.com/news/stories/aiforce-announcement/). Broadly, this is how we apply our expertise at all layers of the Salesforce ecosystem:
AIforce. We talk about the security, cost, and governance tradeoffs of LLMs v. Salesforce AI tools.
Agentforce. We speak to the advantages of deterministic v. probabilistic AI and when to build v. go OOB.
Customer 360. We advocate for the strengths of Salesforce as a platform and explain why vibe-coding a CRM will fail.
Data 360. We define integrations with other systems and create a data architecture that makes sense based on customer priorities.
Baseline RequirementsData & AI
Hands-on experience with cloud data warehouse and lakehouse platforms (Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, or equivalent)
Strong SQL skills and comfort with data modeling across structured, semi-structured, and unstructured data
Practical experience with agentic AI or generative AI
Cloud & Engineering
Deep knowledge of enterprise data platforms and cloud architectures (AWS, GCP, or Azure — including data services, networking, identity, security, and governance)
Data management fundamentals: data modeling, MDM, identity resolution, data quality, governance, and lineage
Integration principles: APIs, event streaming (Kafka/Pub-Sub), ETL/ELT patterns
Principles of network, application, and information security
Communication, Consulting & Logistics
Ability to translate complex business and technical requirements into a compelling solution narrative for executive, technical, and business audiences
Strategic problem solver and thought leader; comfortable at the C-suite level
Lifelong learner - inquisitive, practical, passionate about technology and sharing knowledge
Willing and able to travel domestically (1-2 times per quarter)
Bachelor's degree in Computer Science, MIS, Data Science, Software Engineering, or other STEM field (or equivalent experience)
Preferred Requirements
Lakehouse & Unified Data Architecture: Deep understanding of modern lakehouse and cloud data platform patterns for unifying, harmonizing, and activating enterprise data. This includes designing data pipelines, semantic layers, and feature stores that prepare and enrich data for AI and agent-based applications
Agentic Memory & Context Architecture: Experience designing persistent context layers for AI agents, including how structured and unstructured data feeds agent memory, how data schemas serve as server-side context, and how a unified data platform acts as the persistent knowledge base and scratchpad across agentic loops
Generative AI Architecture: Experience architecting and integrating generative AI solutions into enterprise systems, including API gateways, model management platforms, embedding pipelines, vector databases, and data flows necessary for serving LLMs at scale in production

Employment type
Full time