Principal Forward Deployed Engineer - Analytics

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering 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 Experience We are seeking a strategic and technical Forward Deployed Engineer to lead the implementation of our newest innovations in Agentic Analytics across Tableau Next, Cloud and Server. In this high-impact role, you will bridge the gap between product and customer success. Your primary mission is to partner with our most strategic new customers to architect and deploy their very first agentic analytics use cases and supporting metrics and dashboards. By building robust semantic data models on top of the Salesforce Data360 and incorporating Agentforce topics and actions, you will transform raw data into actionable, conversational insights. You will not only ensure the technical success of these initial launches but also channel critical feedback from the field to our product teams, directly shaping the future of enterprise scale agentic analytics. What You'll Actually Be Doing Architect and Deploy Conversational Agents: Lead the end-to-end technical design and build of bespoke conversational analytics agents, ensuring they are optimized for accuracy, latency, and user relevance. Design Semantic Data Models: Construct and refine complex semantic data layers within Salesforce Data360 to ensure the AI agent can accurately interpret, query, and visualize enterprise data structure. Accelerate Customer Time-to-Value: Act as the primary technical builder during the implementation phase, rapidly prototyping and iterating on solutions to solve the customer’s specific business questions ("Zero to One" builds). Integrate Disparate Data Sources: Engineer scalable data pipelines to ingest, clean, and unify structured and unstructured data from various sources into the Data360 lakehouse environment and canonical model Bridge the Product-Customer Gap: Serve as the technical voice of the customer; document implementation friction points and collaborate with Product Management and Engineering to prioritize feature enhancements and roadmap items. Enable Customer Self-Sufficiency: Conduct technical hand-offs and mentoring sessions with customer data teams to ensure they can maintain, tune, and expand the semantic models and agents after the initial engagement. Troubleshoot Complex Architectures: Perform deep-dive root cause analysis on performance bottlenecks, hallucinations, or data modeling errors to ensure a reliable production-grade experience. Y ou're Our Person If... Professional Experience: 7+ years of technical experience in data engineering, analytics implementation, or software development, with a focus on customer-facing delivery. Data Lakehouse Proficiency: Strong hands-on experience with modern data architectures and SQL, specifically regarding data modeling and schema design. AI & NLP Fundamentals: Practical experience building or deploying solutions that utilize Large Language Models (LLMs), RAG (Retrieval-Augmented Generation), or conversational AI frameworks. Semantic Modeling: A solid understanding of how to map business logic to physical data (semantic layers, ontologies, or metrics layers). Programming Skills: Proficiency in Python for data manipulation and scripting; familiarity with