Business Analyst (Context Layer and Semantic Layer)

Overview

Senior Business Analyst based in India to own the translation of business knowledge (metrics, entities, business rules, domain context) into structured semantic and context layers that AI agents use to reason over enterprise data.

What you'll do

• 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
• Design metric and entity definitions with agent-readability in mind, including unambiguous naming, explicit synonyms/aliases, and disambiguation rules for overloaded business terms
• Partner with AI engineers to structure the semantic layer for tool-calling/function-calling patterns so agents select the right metric, filter, and grain
• Continuously test and tune semantic definitions against agent query failures, misinterpretations, and hallucination patterns
• Design the context layer that supplies agents with business rules, caveats, definitions, data lineage, and usage guardrails 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
• Work with platform teams to version and govern context content so agent behavior stays aligned as business rules evolve
• Partner with AI/ML engineers to evaluate agent outputs against expected business answers and identify root causes when agents misinterpret semantic or contextual inputs
• Build test question sets ("golden queries") representing real business questions to validate semantic and 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 and context engineering efforts by translating business nuance into structured inputs agents can reliably use
• Act as the primary bridge between business domain experts and the AI platform/engineering team, encoding business intent into semantic and context structures
• Collaborate with business stakeholders, data engineers and architects to ensure the underlying data model supports the semantic layer
• Work with data governance teams to ensure semantic and context content meets accuracy, security, and access-control standards (e.g., row-level security respected by agent-generated queries)
• Maintain a living business glossary and context repository 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 and change-management process for updating semantic and context definitions as the business evolves, without degrading agent performance

What you'll need

• 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 versus 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

Nice to have

• 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, including 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

Details

• Location: Pune, India