Business Analyst (Context Layer and Semantic Layer)

Sr. Business Analyst - India (Semantic Layer and Context Layer)

Job Summary

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"

Youll 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 agents "understanding" of the business is accurate, current, and governed.

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 layers 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

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