What You'll Do:

Own the AI-Readiness Layer for Coupa’s Data

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.

Set AI Data Governance Standards and Policy

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.

Partner with AI Delivery & Execution

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 and application, anomaly alerts, per-model cost tracking, and SLAs for downstream consumers.

Pair with AI engineers on data sourcing decisions: which governed asset to draw from, which CDE definitions to honor, how to handle records that fall outside AI-ready criteria, and when to require a human-in-the-loop checkpoint.

Triage AI quality incidents jointly. Trace stale, wrong, or duplicated outputs through the gateway, RAG pipeline, and source records, and route fixes to the team best positioned to resolve them.

What You Will Bring to Coupa:

8+ years of related experience, with at least 5 years as a Data Product Manager, Technical Program Manager, or comparable role owning data products and driving cross-functional implementation.
Track record shipping AI/ML solutions end-to-end in an enterprise environment: scoping with stakeholders, partnering with data and engineering teams, working through ambiguity, and measuring impact post-launch.
Working knowledge of LLM application patterns: prompt engineering, retrieval-augmented generation (RAG), embeddings, and agent frameworks. Familiarity with the common failure modes (hallucination, prompt injection, data leakage, training-data contamination).
Solid grounding in data governance principles and frameworks (DAMA-DMBOK, DCAM, or comparable). Hands-on history with data quality standards, CDE frameworks, source-of-truth designations, or metadata/catalog programs.
Familiarity with API governance platforms (the policy enforcement layer that sits in front of API traffic and applies authentication, rate limits, content filtering, and audit logging). Comfortable owning gateway strategy as a product and partnering with engineering on implementation.
SQL fluency sufficient to profile data quality directly against warehouse tables (Snowflake or comparable).
Working familiarity with GDPR, CCPA, and emerging AI regulation (EU AI Act, NIST AI RMF) at the level needed to apply them; legal-grade depth isn’t required.
Strong written communication. The role authors policies, playbooks, and decision memos that cross functional lines.