Lead Data Management Specialist

Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.

Why join Coupa?

Pioneering Technology:
At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend.

Collaborative Culture:
We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence.

Global Impact:
Join a company where your work has a global, measurable impact on our clients, the business, and each other.

Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa.

The Impact of a Lead Data Management Specialist at Coupa:

Coupa is hiring a Technical Lead, AI Data Governance to own the data governance and gateway strategy that makes the company’s growing portfolio of AI tools and agents safe to ship. The role makes sure the data flowing into Coupa’s GenAI and agentic applications is governed, trusted, and fit for purpose, and that the API gateway in front of LLM traffic enforces the policies that protect the company.

This is a senior individual contributor role reporting to the Director, Data Governance. The role partners daily with Enterprise Data Management, AI Delivery & Execution, and Info Sec.

• Resolve the data fitness questions AI builders raise day-to-day: which data sources fit which AI use case, what can safely go into a prompt, which dataset is the source of truth for a given attribute, and what meets the freshness or accuracy bar for retrieval. The AI-readiness framework and source-of-truth designations this role owns produce those answers.
• Define what AI-readiness means for an enterprise data asset (completeness, freshness, accuracy, lineage, consent, licensing, source-of-truth designation), and publish that framework as Coupa’s standard.
• Drive extension of the data catalog into an AI asset inventory. Rate each in-scope asset as AI-ready, AI-restricted, or AI-prohibited, with documented criteria, owners, and review cadence.
• Partner with Enterprise Data Management to ensure foundational data quality refreshes (third-party account and contact enrichment, dedupe, taxonomy, address standardization) flow downstream into the vector stores, RAG indexes, and prompt-time lookups AI applications depend on.
• Connect the metric library to AI consumers: every number an AI tool surfaces traces back to a governed metric definition, with documented and testable lineage.
• Define AI-aware data quality monitoring requirements.

• Author and maintain Coupa’s AI data governance playbook: acceptable use, data classification for AI consumption, prompt logging, human-in-the-loop expectations, and model deprecation policies.
• Build and run intake and review for new AI use cases. Evaluate data sensitivity, source quality, source-of-truth alignment, and gateway requirements before launch. Target turnaround: five business days.
• Translate compliance and regulatory requirements (GDPR, SOC 2, EU AI Act, NIST AI RMF) into operational standards the data and AI teams can apply.

• Embed in the AI Delivery & Execution team’s planning rituals, sprint reviews, and architecture discussions on a regular cadence.
• Co-own the AI/API gateway. Define the governance policies, target outcomes, and observability requirements; the AI Delivery engineering team builds the applications that integrate with it.
• Define gateway-level controls for PII detection and redaction, prompt sanitization, content filtering, and prompt/response logging. Translate compliance and policy decisions into specifications engineering can build.
• Define observability and reporting requirements for the gateway: usage by team…