Technical Architect 8

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Job Category
Sales

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

The Data & AI Cloud Technical Architect is a pre-sales, customer-facing enterprise domain expert who combines deep data platform knowledge with broad technical skills, industry acumen, and business strategy savvy. This specialist role sits at the intersection of modern data architecture and AI — helping customers navigate their most complex data and AI challenges, from unified data platforms and lakehouses to agentic AI systems and LLM-powered applications.
The Data and AI Technical Architect is a subject matter expert in one or more of the following areas: data architecture & engineering, data lakehouse & cloud warehouse platforms, enterprise data management, identity resolution, machine learning & AI, integration, cloud computing, security, application architecture, analytics, and agentic AI . With a solid blend of technical depth and consultative skill, this role uses a structured discovery approach to uncover business and technical requirements — and translate them into winning architectures.
This architect brings a broad background spanning cloud data platforms, data engineering, ML, and solution architecture. They lead key technical and business discussions around enterprise data and AI programs — from strategy through architecture to deployment — and determine which technologies and patterns best serve the customer's goals, drawing on deep platform knowledge, industry experience, and cloud-native best practices.
The Data & AI Cloud Technical Architect also helps sales teams develop specific, repeatable propositions and go-to-market strategies. They participate in delivering solution best practices, reference architectures, enablement sessions, and industry summits for customers, partners, and internal audiences.

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; familiarity with dbt or Spark a plus

• Familiarity with ML fundamentals: feature engineering, model training pipelines, inference patterns, and vector/embedding-based retrieval

• Practical experience with agentic AI or generative AI — built something with LLMs or agents, whether in production, a POC, or a side project

• Comfortable using AI coding tools (Copilot, Cursor, Claude Code, or similar) to build prototypes and demos quickly; Python proficiency strongly preferred

Cloud & Engineering

• Deep knowledge of enterprise data platforms and cloud architectures (AWS, GCP, or Azure — including data services, networking, identity, 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

• Process orchestration and automation

• Principles of network, application, and information security

• Willingness to work with code (Python, SQL, JavaScript, Java, or similar)

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

• Strong written, verbal, and presentation skills

• Excellent time management across multiple concurrent engagements

• Lifelong learner — inquisitive, practical, passionate about technology and sharing knowledge

• Willing and able to travel domestically

• Bachelor's degree in Computer Science, MIS, Data Science, Software Engineering, or other STEM field — or equivalent experience. Graduate study a plus.

Preferred Requirements

• Experience working as a data architect, solutions engineer, cloud architect, IT consultant, or developer in a customer-facing role delivering differentiated data and AI solutions. We welcome a variety of backgrounds.

• Hands-on experience building or administering cloud data platforms — warehouse/lakehouse environments (Snowflake, Databricks, BigQuery), data pipelines, and ML platforms

• Experience designing or operating ML workflows : training, experimentation, deployment, and monitoring (SageMaker, Vertex AI, Azure ML, Databricks MLflow, or equivalent)

• Experience with data governance frameworks , compliance, privacy (PII/GDPR/CCPA), and risk mitigation in data-intensive environments

• Experience with design thinking, persona-based discovery, or other innovation and workshop facilitation techniques

• Proven experience in a specific industry vertical or market segment is a plus

• Familiarity with the Salesforce platform is a plus — not a prerequisite

• Required Qualifications

• B.S Computer Science, Software Engineering, MIS

• Knowledge of related applications, relational databases, and ERP technologies

• Strong oral, written, presentation, collaboration, and interpersonal communication skills

• Ability to work as part of a team to solve technical problems in varied political environments

• Minimum of 4 years of professional experience.

Agentic AI, Generative AI & Platform Skills

The ideal candidate will have hands-on experience with the following capabilities — critical for designing modern, AI-powered enterprise data solutions. We value depth in the underlying concepts and architectures over familiarity with any specific vendor platform:

• Agentic AI Systems : Hands-on experience developing, deploying, and managing agentic AI systems — ideally including production or POC deployments. Practical understanding of how to design autonomous agents that can plan, reason, use tools, and interact with heterogeneous data systems including cloud warehouses, APIs, and vector stores. Experience with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent

• LLM Fluency & Prompt Engineering : Deep, working understanding of how large language models function — tokenization, context windows, temperature, grounding, hallucination mitigation, and tradeoffs between hosted models (OpenAI, Anthropic, Gemini) and open-weight alternatives (Llama, Mistral). Proven ability to design and optimize prompts using chain-of-thought, few-shot, system prompts, tool calling, and RAG patterns

• 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

• 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