Business Analyst (Context Layer and Semantic Layer)

Sr. Business Analyst – India (Semantic Layer and Context Layer ) About SailPoint: Love what you do. And love where you do it. Smart people, fun culture, innovative work, beautiful offices — oh, and everyone’s really nice. That’s what people say about SailPoint. We’re known as the company where everyone wants to work, and employee reviews about us prove it. If you’re passionate about  AI Context Layer, Semantic Layer modeling and working at a company where you  can truly have an impact, join our team. SailPoint empowers the largest, most complex organizations by putting identity at the Centre of Security and IT. Our 2,000+ customers include global financial institutions, government entities, and pharmaceutical organizations and more. About the Role We are building the semantic and contextual foundation that powers our AI agent and agentic analytics platform. As Senior Business Analyst, you will own the translation of business knowledge — metrics, entities, business rules, and domain context — into structured, machine-consumable semantic and context layers that AI agents rely on to reason over enterprise data accurately and safely. Unlike a traditional BI-focused semantic layer built for dashboards, this role treats the semantic layer as the "vocabulary" and the context layer as the "grounding Business context knowledge Base " that an LLM-powered agent uses at query/inference time — to disambiguate business terms, retrieve the right data, apply correct business logic, and avoid hallucinated or incorrect answers when a business user asks a natural-language question like "What was our churn last quarter by region?" You'll sit at the intersection of business analysis, data architecture, and applied AI — working closely with AI/ML engineers, platform architects, and business stakeholders to ensure the agent's "understanding" of the business is accurate, current, and governed. Key Responsibilities Semantic Layer for Agent Consumption Define and maintain the semantic model (metrics, dimensions, entities, synonyms, relationships) that AI agents use to map natural-language business questions to correct data queries (SQL, semantic layer APIs, or knowledge graph traversals) Design metric and entity definitions with agent-readability in mind: unambiguous naming, explicit synonyms/aliases, disambiguation rules for overloaded business terms (e.g., "revenue" meaning different things in different departments) Partner with AI engineers to structure the semantic layer for tool-calling/function-calling patterns — ensuring agents select the right metric, filter, and grain when generating queries Continuously test and tune semantic definitions against agent query failures, misinterpretations, and hallucination patterns Context Layer & Grounding for AI Agents