Procurement Data Strategy and Analytics Lead

OpenAI•San Francisco, CA

1d•Hybrid

About The Position

OpenAI’s Procurement Center of Excellence (COE) is building an AI-enabled procurement analytics and reporting foundation so leaders and operators can make decisions with trusted data, and enable automation with clean, policy-aligned inputs. This role sits at the center of that effort: unifying data across our procurement stack, owning definitions/governance, and turning messy operational signals into durable self-serve insights. We’re looking for a Procurement Data Strategy & Analytics Senior Manager to architect and own the end-to-end procurement data foundation—from master data governance to production-grade pipelines and dashboards—spanning key Procurement, T&E, and Extended Workforce tooling. You’ll build and maintain trusted procurement dashboards from well curated datasets—turning spend, supplier, and cycle-time data into self-serve insights that improve decision-making, user experience, and compliance visibility (and that power automation and GPT-agent workflows). This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

Requirements

Have 8+ years of experience building data strategy, analytics, and/or data products for Procurement, Finance Ops, Supply Chain, or adjacent operational domains.
Have a proven track record of partnering with upstream technical teams to design a procurement data model end-to-end (supplier, spend, contract, intake/workflow, PO/invoice/payment, T&E, contingent workforce), including KPI definitions and canonical datasets.
Have hands-on experience building trusted datasets and dashboards that enable self-serve insights—turning spend, supplier, and cycle-time data into decision-ready reporting and compliance visibility.
Are an expert at creating compelling data visualization with dashboarding tools (e.g. Tableau, Zip, Navan, VNDLY, Oracle).
Have experience with SQL and familiarity with analytics engineering concepts (data modeling, dimensional modeling, ELT/ETL patterns, testing/monitoring, documentation) so as to be a good XF partner.
Enforce high standards upon partner data/engineering teams to build production-grade pipelines (e.g., Databricks or similar) with data quality checks, lineage, and governance.
Have expertise in procurement master data management: supplier/vendor master, taxonomy/category hygiene, item/service classification, entity/cost center alignment, and deduplication/normalization.
Have demonstrated success establishing data governance: metric definitions, source-of-truth alignment, access controls, change management, and audit-ready traceability.
Have a strong track record translating ambiguous business needs into clear analytics requirements and together shipping data products iteratively with measurable impact (cycle time, rework reduction, SLA adherence, compliance).
Have excellent cross-functional collaboration skills—able to align Procurement, Finance, Legal, Security, and Enterprise Tech on definitions, workflows, and reporting.
Have the ability to operate in a high-growth environment: high ownership, fast execution, comfort with ambiguity, and a bias toward automation and scalable solutions.

Responsibilities

Partner with our Enterprise Systems & Platform Team to architect a unified procurement data mart spanning Zip, Oracle, Ironclad, VNDLY, Navan, Salesforce, and analytics tools—so Procurement can operate with real-time, trusted intelligence.
Partner with our Enterprise Systems & Platform Team to build production-grade datasets and pipelines (e.g., in Databricks or equivalent), with consistent join logic, normalized tables, and data quality controls that hold up at scale.
Own procurement master data and governance (suppliers, categories, items, entities, cost centers): design and enforce schema, validation logic, taxonomy alignment, and consistent definitions across systems.
Design the procurement data layer that powers AI and automation — ensuring structured, validated datasets that enable GPT agents, workflow automation, anomaly detection, and intelligent routing across procurement operations.
Deliver dashboards + self-serve reporting Source-to-Contract, Procure-to-Pay, T&E, and Extended Workforce. Sample metrics include: supplier spend and performance insights intake SLAs / approval velocity / cycle time audit flags, exceptions, and compliance visibility escalations, rework, and workflow bottlenecks
Enable AI agents and procurement automation by ensuring clean, validated datasets—so intelligent workflows operate reliably and don’t stall due to incomplete or misaligned inputs.
Harmonize KPIs and metric definitions across Sourcing, P2P, T&E, and Extended Workforce and act as the central owner of procurement metrics, KPI definitions, monthly reporting, and analytics governance.
Monitor data health + close the loop operationally: resolve mismatches and exceptions, reduce supplier duplication/category misclassification, and eliminate recurring “manual triage” caused by bad data.
Drive analytics adoption and be a champion for data maturity: replace ad hoc spreadsheets with governed pipelines and dashboards; publish enablement materials and measurement plans so XFN partners actively use Procurement intelligence.