{"id":3214182,"url":"https://alion.io/job/sailpoint-business-analyst-context-layer-and-semantic-layer","title":"Business Analyst (Context Layer and Semantic Layer)","company":{"id":4926,"name":"SailPoint","domain":"sailpoint.com","url":"https://alion.io/company/sailpoint","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":26,"ghost_share":0,"stale_share":0.654,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":17500,"max_usd":35000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":25},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Alation","optional":true},{"name":"Collibra","optional":true},{"name":"dbt","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"LookML","optional":true},{"name":"Neo4j","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-10-24T17:38:56Z","employer_posted_date":null,"last_verified_at":"2026-10-24T17:38:56Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"Sr. Business Analyst – India (Semantic Layer and Context Layer )\n\nAbout SailPoint:\nLove 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.\nAbout the RoleWe 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.\nUnlike 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\nYou'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.\nKey ResponsibilitiesSemantic Layer for Agent Consumption\nDefine 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)\nDesign 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)\nPartner 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\nContinuously test and tune semantic definitions against agent query failures, misinterpretations, and hallucination patterns\n\nContext Layer & Grounding for AI Agents\nDesign 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)\nCurate 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)\nDefine context scoping and prioritization rules so agents retrieve the most relevant and correct context per query, minimizing noise and conflicting information\nWork with platform teams to version and govern context content so agent behavior stays aligned as business rules evolve\n\nAgent Behaviour & Quality Assurance\nPartner with AI/ML engineers to evaluate agent outputs against expected business answers; identify root causes when agents misinterpret semantic or contextual inputs\nBuild test question sets (golden queries) representing real business questions to validate semantic/context layer coverage and accuracy\nDefine feedback loops where incorrect agent responses trace back to semantic gaps, missing context, or ambiguous definitions — and drive remediation\nSupport prompt/context engineering efforts by translating business nuance into structured inputs agents can reliably use\n\nCross-Functional Collaboration\nAct 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\nCollaborate with Business Stakeholder, data engineers and architects to ensure the underlying data model supports the semantic layer the agent depends on\nWork 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)\n\nDocumentation & Governance\nMaintain a living business glossary and context repository that serves as the authoritative source for both human users and AI agents\nDocument the semantic layer's coverage, known gaps, and disambiguation logic for auditability\nEstablish a review/change-management process for updating semantic and context definitions as the business evolves, without degrading agent performance\nRequired Qualifications6+ years as a Business Analyst, Data Analyst, or Knowledge/Ontology Analyst, with recent exposure to AI/LLM-powered platforms, copilots, or agentic systems\nStrong grasp of semantic modeling concepts (metrics layers, ontologies, knowledge graphs) and how they differ when designed for machine/agent consumption vs. human dashboards\nUnderstanding of how LLMs consume structured context — RAG architectures, embeddings, chunking strategies, function/tool calling, prompt/context engineering\nExperience translating ambiguous business language into precise, structured, disambiguated definitions\nStrong SQL skills and ability to work with data models, APIs, or knowledge graphs that agents query against\nExperience writing and structuring documentation (glossaries, business rules, metadata) in formats consumable by both people and AI systems\nExcellent stakeholder management — able to extract and reconcile business logic from multiple domain experts\nPreferred QualificationsHands-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)\nExperience with vector databases and RAG pipeline design (such as Pinecone, Weaviate, pgvector, etc.)\nFamiliarity with knowledge graph technologies (Neo4j, RDF/OWL) for entity/relationship modeling\nExperience evaluating LLM/agent outputs — building eval harnesses, golden datasets, or human-in-the-loop review processes\nBackground in AI governance, model risk, or responsible AI practices as applied to enterprise data\nExposure to enterprise data catalogs (Collibra, Alation, Atlan, Purview) and metadata management\nWhat Success Looks LikeAI agents consistently interpret business questions correctly because the semantic layer resolves ambiguity before the query is even generated\nAgent responses are grounded in accurate, current business context — reducing hallucinations and incorrect metric usage\nBusiness stakeholders trust agent-generated insights because the underlying semantic/context layer is transparent, auditable, and governed\nA measurable reduction in agent query failures/misinterpretations over time, tracked via golden-query test suites\n\nThis 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.\n30-60-90 Day Plan: Sr. Business Analyst – Semantic & Context Layer for AI Agents\nPhase 1: Days 1–30 — Learn, Map, AssessTheme: Understand the business, the data, the current AI agent/platform state, and where the gaps are.\nKey Activities\nMeet 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)\nMeet 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\nInventory existing semantic assets: BI semantic layers, data dictionaries, business glossaries, metric definitions — assess quality, coverage, and consistency\nRun/observe the AI agent (if live) against a sample of real business questions; log where it fails, hallucinates, or misinterprets terms\nReview existing data models, lineage documentation, and access/security controls relevant to agent queries\nIdentify 3–5 high-value, high-ambiguity business domains to prioritize (e.g., Marketing, Salesforce (L2C), revenue, churn, customer segmentation)\n\nDeliverables\nCurrent-state assessment document: semantic layer maturity, context layer gaps, agent failure patterns\nInitial glossary of top ambiguous/overloaded business terms\nInitial Context Layer and Semantic Layer – List of Objects, Tables , Fiels , Domain Terms, Rules, process if any , Metrics and Formulae.\nStakeholder map (who owns which metrics/definitions)\nList of 15–20 golden queries collected from real business users to use as early test cases\nComplete 3 Pilot usecases for Context Layer\n\nSuccess Markers\nClear understanding of where agent inaccuracy stems from (semantic gaps vs. context gaps vs. data model issues)\nRelationships established with AI engineering and key business stakeholders\n\nPhase 2: Days 31–60 — Design, Build, TestTheme: Start building and iterating on the semantic/context layer for the priority domains identified in Phase 1.\nKey Activities\nDefine structured metric and entity specs for priority domains: name, definition, synonyms/aliases, calculation logic, grain, owner\nDesign disambiguation rules for known overloaded terms (e.g., department-specific revenue definitions)\nStructure context objects (business rules, caveats, exceptions) for RAG retrieval — work with AI engineers on chunking, tagging, and metadata format\nRun the golden-query set against the agent before and after semantic/context updates; document accuracy shifts\nSet up a lightweight review/change-management process for updating definitions without breaking agent behavior\nBegin building the living business glossary / context repository as the shared source of truth\nEstablish a feedback loop: agent failure → root cause tagged (semantic gap, context gap, data issue) → routed to fix owner\n\nDeliverables\nSemantic layer v1 for 3–5 priority domains, live in the agent's query/tool-calling logic\nContext layer v1 (structured business rules/definitions) integrated into RAG or prompt context\nExpanded golden-query test suite (40–50 queries) with pass/fail tracking\nFirst iteration of the governance/change-management process doc\n\nSuccess Markers\nMeasurable improvement in agent accuracy on golden queries for priority domains (target: define a baseline % and improvement goal, e.g., 60% → 80%)\nBusiness stakeholders in priority domains have reviewed and signed off on definitions\nA repeatable process exists for adding new metrics/context to the layer\n\nPhase 3: Days 61–90 — Scale, Govern, Prove ValueTheme: Expand coverage, formalize governance, and demonstrate measurable impact to stakeholders.\nKey Activities\nExpand semantic/context coverage to additional business domains beyond the initial priority set\nFormalize the golden-query evaluation as an ongoing QA process (e.g., run weekly/bi-weekly, track trend over time)\nPresent before/after agent accuracy results to leadership and stakeholders, using concrete examples of resolved ambiguity/hallucination\nFinalize governance model: who approves new metric definitions, how versioning works, how context gets deprecated/updated as business rules change\nTrain business users and analysts on how to contribute to or query the glossary/context repository\nDocument known limitations and a roadmap for the next 90 days (e.g., additional domains, knowledge graph expansion, more advanced disambiguation)\n\nDeliverables\nDocumented, governed semantic + c...","description_format":"text","description_chars":13309,"description_truncated":true,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Identity Management"],"lifecycle":[{"event":"open","at":"2026-10-24T20:09:15Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely 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