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.

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.

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

• Design the context layer that supplies agents with the business rules, caveats, definitions, data lineage, and usage guardrails needed to ground responses (e.g., via RAG retrieval, system prompts, or structured metadata injection)
• Curate and structure business context objects — glossaries, policy documents, calculation logic, exceptions/edge cases — into formats optimized for retrieval (chunking strategy, embeddings-friendly structure, metadata tagging)
• Define context scoping and prioritization rules so agents retrieve the most relevant and correct context per query, minimizing noise and conflicting information
• Work with platform teams to version and govern context content so agent behavior stays aligned as business rules evolve
  
  

Agent Behaviour & Quality Assurance

• Partner with AI/ML engineers to evaluate agent outputs against expected business answers; identify root causes when agents misinterpret semantic or contextual inputs
• Build test question sets (golden queries) representing real business questions to validate semantic/context layer coverage and accuracy
• Define feedback loops where incorrect agent responses trace back to semantic gaps, missing context, or ambiguous definitions — and drive remediation
• Support prompt/context engineering efforts by translating business nuance into structured inputs agents can reliably use
  
  

Cross-Functional Collaboration

• Act as the primary bridge between business domain experts and the AI platform/engineering team, ensuring business intent is faithfully encoded into semantic and context structures
• Collaborate with Business Stakeholder, data engineers and architects to ensure the underlying data model supports the semantic layer the agent depends on
• Work with data governance teams to ensure context/semantic content meets accuracy, security, and access-control standards (e.g., row-level security respected by agent-generated queries)
  
  

Documentation & Governance

• Maintain a living business glossary and context repository that serves as the authoritative source for both human users and AI agents
• Document the semantic layer's coverage, known gaps, and disambiguation logic for auditability
• Establish a review/change-management process for updating semantic and context definitions as the business evolves, without degrading agent performance

• 6+ years as a Business Analyst, Data Analyst, or Knowledge/Ontology Analyst, with recent exposure to AI/LLM-powered platforms, copilots, or agentic systems
• Strong grasp of semantic modeling concepts (metrics layers, ontologies, knowledge graphs) and how they differ when designed for machine/agent consumption vs. human dashboards
• Understanding of how LLMs consume structured context — RAG architectures, embeddings, chunking strategies, function/tool calling, prompt/context engineering
• Experience translating ambiguous business language into precise, structured, disambiguated definitions
• Strong SQL skills and ability to work with data models, APIs, or knowledge graphs that agents query against
• Experience writing and structuring documentation (glossaries, business rules, metadata) in formats consumable by both people and AI systems
• Excellent stakeholder management — able to extract and reconcile business logic from multiple domain experts

• Hands-on experience with semantic layer tools (such as UnifyApp, Atlan, dbt Semantic Layer, Cube, AtScale, LookML) and AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or similar)
• Experience with vector databases and RAG pipeline design (such as Pinecone, Weaviate, pgvector, etc.)
• Familiarity with knowledge graph technologies (Neo4j, RDF/OWL) for entity/relationship modeling
• Experience evaluating LLM/agent outputs — building eval harnesses, golden datasets, or human-in-the-loop review processes
• Background in AI governance, model risk, or responsible AI practices as applied to enterprise data
• Exposure to enterprise data catalogs (Collibra, Alation, Atlan, Purview) and metadata management

• AI agents consistently interpret business questions correctly because the semantic layer resolves ambiguity before the query is even generated
• Agent responses are grounded in accurate, current business context — reducing hallucinations and incorrect metric usage
• Business stakeholders trust agent-generated insights because the underlying semantic/context layer is transparent, auditable, and governed
• A measurable reduction in agent query failures/misinterpretations over time, tracked via golden-query test suites
  
  

This role defines what the agent should know and how it's structured (business/semantic ownership), while assuming AI/ML engineers own the underlying retrieval and orchestration infrastructure.

Theme: Understand the business, the data, the current AI agent/platform state, and where the gaps are.

Key Activities

• Meet with Busienss Stakeholders, AI/ML engineering, data platform, and architecture teams to understand the current agent stack (LLM used, orchestration framework, RAG setup, semantic layer tooling if any)
• Meet with business stakeholders across key domains (Finance, Sales, Ops, Marketing , IT, HR, GTM, etc.) to understand how they currently define and use core metrics/entities
• Inventory existing semantic assets: BI semantic layers, data dictionaries, business glossaries, metric definitions — assess quality, coverage, and consistency
• Run/observe the AI agent (if live) against a sample of real business questions; log where it fails, hallucinates, or misinterprets terms
• Review existing data models, lineage documentation, and access/security controls relevant to agent queries
• Identify 3–5 high-value, high-ambiguity business domains to prioritize (e.g., Marketing, Salesforce (L2C), revenue, churn, customer segmentation)
  
  

Deliverables

• Current-state assessment document: semantic layer maturity, context layer gaps, agent failure patterns
• Initial glossary of top ambiguous/overloaded business terms
• Initial Context Layer and Semantic Layer – List of Objects, Tables , Fiels , Domain Terms, Rules, process if any , Metrics and Formulae.
• Stakeholder map (who owns which metrics/definitions)
• List of 15–20 golden queries collected from real business users to use as early test cases
• Complete 3 Pilot usecases for Context Layer
  
  

Success Markers

• Clear understanding of where agent inaccuracy stems from (semantic gaps vs. context gaps vs. data model issues)
• Relationships established with AI engineering and key business stakeholders
  
  

Theme: Start building and iterating on the semantic/context layer for the priority domains identified in Phase 1.

Key Activities

• Define structured metric and entity specs for priority domains: name, definition, synonyms/aliases, calculation logic, grain, owner
• Design disambiguation rules for known overloaded terms (e.g., department-specific revenue definitions)
• Structure context objects (business rules, caveats, exceptions) for RAG retrieval — work with AI engineers on chunking, tagging, and metadata format
• Run the golden-query set against the agent before and after semantic/context updates; document accuracy shifts
• Set up a lightweight review/change-management process for updating definitions without breaking agent behavior
• Begin building the living business glossary / context repository as the shared source of truth
• Establish a feedback loop: agent failure → root cause tagged (semantic gap, context gap, data issue) → routed to fix owner
  
  

Deliverables

• Semantic layer v1 for 3–5 priority domains, live in the agent's query/tool-calling logic
• Context layer v1 (structured business rules/definitions) integrated into RAG or prompt context
• Expanded golden-query test suite (40–50 queries) with pass/fail tracking
• First iteration of the governance/change-management process doc
  
  

Success Markers

• Measurable improvement in agent accuracy on golden queries for priority domains (target: define a baseline % and improvement goal, e.g., 60% → 80%)
• Business stakeholders in priority domains have reviewed and signed off on definitions
• A repeatable process exists for adding new metrics/context to the layer
  
  

Theme: Expand coverage, formalize governance, and demonstrate measurable impact to stakeholders.

Key Activities

• Expand semantic/context coverage to additional business domains beyond the initial priority set
• Formalize the golden-query evaluation as an ongoing QA process (e.g., run weekly/bi-weekly, track trend over time)
• Present before/after agent accuracy results to leadership and stakeholders, using concrete examples of resolved ambiguity/hallucination
• Finalize governance model: who approves new metric definitions, how versioning works, how context gets deprecated/updated as business rules change
• Train business users and analysts on how to contribute to or query the glossary/context repository
• Document known limitations and a roadmap for the next 90 days (e.g., additional domains, knowledge graph expansion, more advanced disambiguation)
  
  

Deliverables

• Documented, governed semantic + context layer covering majority of high-value business domains
• Ongoing QA/eval process with historical accuracy tracking
• Stakeholder-facing readout: impact summary (accuracy improvement, reduced hallucinations, adoption metrics)
• 90-day forward roadmap
  
  

Success Markers

• Agent demonstrates sustained, measurable accuracy improvement across expanded domains
• Governance process is operating without the BA being a bottleneck for every change
• Leadership/stakeholders have visibility into and confidence in the semantic/context layer's value
  
  

SailPoint is an equal opportunity employer and we welcome all qualified candidates to apply to join our team. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other category protected by applicable law.

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