Technical Architect 8

Salesforce•Austin, MI

5d•$190,750 - $255,150•Remote

About The Position

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.

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 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
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)
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.
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 with Agentic AI Systems: 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, and feature stores that prepare and enrich data for AI and agent-based applications — covering structured, semi-structured, and unstructured data
Process Orchestration & Workflow Automation: 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

Nice To Haves

Familiarity with dbt or Spark a plus
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

Responsibilities

Combine deep data platform knowledge with broad technical skills, industry acumen, and business strategy savvy.
Help customers navigate complex data and AI challenges, from unified data platforms and lakehouses to agentic AI systems and LLM-powered applications.
Act as a subject matter expert in areas such as 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.
Use a structured discovery approach to uncover business and technical requirements and translate them into winning architectures.
Lead key technical and business discussions around enterprise data and AI programs.
Determine which technologies and patterns best serve the customer's goals.
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 working demos, reference architectures, and code.
Go deep on customer problems before prescribing solutions.
Share learnings internally, with customers, and at industry events.
Engage critically with new technology and form views based on evidence.