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About the Role

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.

Responsibilities

Translate complex business and technical requirements into a compelling solution narrative for executive, technical, and business audiences.
Lead key technical and business discussions around enterprise data and AI programs from strategy through architecture to deployment.
Determine which technologies and patterns best serve the customer's goals, drawing on deep platform knowledge, industry experience, and cloud-native best practices.
Help sales teams develop specific, repeatable propositions and go-to-market strategies.
Participate in delivering solution best practices, reference architectures, enablement sessions, and industry summits for customers, partners, and internal audiences.
Develop, deploy, and manage agentic AI systems, including production or POC deployments.
Design autonomous agents that can plan, reason, use tools, and interact with heterogeneous data systems.
Design and optimize prompts using chain-of-thought, few-shot, system prompts, tool calling, and RAG patterns.
Design persistent context layers for AI agents, including how structured and unstructured data feeds agent memory.
Architect and integrate generative AI solutions into enterprise systems.
Design data pipelines, semantic layers, and feature stores that prepare and enrich data for AI and agent-based applications.
Design orchestration layers for complex, multi-step business processes that trigger agent actions and manage model responses.

Requirements

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 is 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.
Comfortable using AI coding tools (Copilot, Cursor, Claude Code, or similar) to build prototypes and demos quickly.
Python proficiency strongly preferred.
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).
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.
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.
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.
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.
Hands-on experience developing, deploying, and managing agentic AI systems.
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.
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.
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.
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.
Deep understanding of modern lakehouse and cloud data platform patterns for unifying, harmonizing, and activating enterprise data.
Experience designing orchestration layers for complex, multi-step business processes — including workflows that trigger agent actions, handle model responses, manage state, and coordinate data interactions across heterogeneous systems.

Skills

Data & AI
Cloud data warehouse and lakehouse platforms
SQL
Data modeling
ML fundamentals
Agentic AI
Generative AI
LLMs
AI coding tools
Python
Cloud architectures
Enterprise data platforms
Data management
Integration principles
APIs
Event streaming
ETL/ELT patterns
Process orchestration
Automation
Network security
Application security
Information security
Communication
Consulting
Problem solving
Time management
Agentic AI Systems
LLM Fluency
Prompt Engineering
Agentic Memory & Context Architecture
Generative AI Architecture
Lakehouse & Unified Data Architecture
Process Orchestration & Workflow Automation

Location

Domestic

Work Type

Customer-facing
Pre-sales

Experience Level

Minimum of 4 years of professional experience

Education Level

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

Salary/Compensations

$173,460 - $231,980 annually
$190,750 - $255,150 per year

Benefits

Time off programs
Medical
Dental
Vision
Mental health support
Paid parental leave
Life and disability insurance
401(k)
Employee stock purchasing program

About the Company

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?
Agentforce is the future of AI, and you are the future of Salesforce.

Equal Opportunity

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment.
At Salesforce, we believe in equality for all.
We believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination.
Know your rights: workplace discrimination is illegal.
Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law.
This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey.
It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between.
Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit.
The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.