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Coupa is seeking a Solution Analyst for Agentic Integrations to bridge business requirements, solution architecture, and AI-driven integrations. This role sits at the intersection of business stakeholders, engineering teams, and Coupa's curated Snowflake data platform, translating high-level business needs into detailed, buildable integration specifications.
You will gather and translate business requirements from Finance, Procurement, and IT stakeholders into agentic solution designs. You'll elaborate solution direction into detailed technical specifications—field-level data mappings, transformation logic, error handling, and edge cases—before engineering begins. You'll partner closely with the Data/Analytics team to define consumption requirements: which curated tables, refresh cadence, and access scope each agent use case needs.
A key differentiator of this role: you write code directly on integrations when doing so is faster than a full handoff, working alongside Integration Engineers. You'll produce solution blueprints (data flow, business logic, acceptance criteria) for each new agentic use case, define data contracts and semantic mappings between Snowflake datasets and MCP tool schemas, and validate technical feasibility with the Technical Architect.
You own use-case-level acceptance criteria, ensuring delivered connectors and agents meet business intent. You'll maintain a living catalog of curated data domains and their mapped agent use cases, and serve as the primary liaison between the integration team and business/data stakeholders. This is a hands-on role that requires both strategic thinking and technical execution.
Coupa is a global spend management platform powered by AI, serving 10M+ buyers and suppliers. The company emphasizes innovation, collaboration, and global impact.
REQUIREMENTS:
• 6+ years in solution/business analysis, integration engineering, or similar role bridging business and engineering teams
• Proficient in Python or TypeScript/Node.js—comfortable writing and shipping working code, not just specifying it
• Strong SQL and hands-on experience with Snowflake (or comparable cloud data warehouse)—data models, views, semantic layers
• Track record translating ambiguous business requirements into detailed technical specifications engineering teams can build from directly
• Working familiarity with agent/LLM tool-calling concepts (MCP or similar)—enough to scope feasible use cases and their detailed data flows
• Understanding of data governance, access control, and data-quality concepts for curated analytical layers
• Excellent stakeholder-facing communication in English; comfortable running requirements workshops with business leaders
