Business Analyst (Context Layer and Semantic Layer)

SailPoint Technologies · Software & Internet

Pune, India
On-site
Posted today
Consulting & Strategy

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About the job

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

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

Required Qualifications

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

Preferred Qualifications

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

What Success Looks Like

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.

30-60-90 Day Plan: Sr. Business Analyst – Semantic & Context Layer for AI Agents

Phase 1: Days 1–30 — Learn, Map, Assess

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

Phase 2: Days 31–60 — Design, Build, Test

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

Phase 3: Days 61–90 — Scale, Govern, Prove Value

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.

Alternative methods of applying for employment are available to individuals unable to submit an application through this site because of a disability. Contact applicationassistance@sailpoint.com or mail to 11120 Four Points Dr, Suite 100, Austin, TX 78726, to discuss reasonable accommodations. NOTE: Any unsolicited resumes sent by candidates or agencies to this email will not be considered for current openings at SailPoint.

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