Technical Architect, SMB Pre-Sales

Leadout Capital; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Leadout Capital is seeking an SMB Technical Architect to drive pre-sales engagement for Salesforce AI, Data, and Security solutions. The role requires aligning customer data, AI transformation strategies, and architecture decisions with sales pursuits.

The candidate will articulate data, integration, and security architectures, develop repeatable propositions, and partner with sales to accelerate customer outcomes.

Qualifications

• Hands-on experience with cloud data warehouses/lakehouses.
• Strong SQL and data modeling across varied data formats.
• Experience with agentic or generative AI in production.
• Ability to translate complex requirements into solutions for executives.

Responsibilities

• Act as pre-sales, customer-facing expert for Salesforce AI, Data, and Security solutions.
• Define architectures across data, integration, security, and AI for customers.
• Develop repeatable propositions and go-to-market strategies for sales teams.
• Communicate solutions to executive and technical audiences.

Skills

Cloud data warehouse

Lakehouse platforms

SQL

Data modeling

Agentic AI

Generative AI

Architecture design

Executive storytelling

Travel willingness

Education

Bachelor's degree in Computer Science or related

Tools

Snowflake

Databricks

BigQuery

Redshift

Azure Synapse

Job description

The Role

Summary

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 Requirements

Data & 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