Drimva

i360technologies

Oracle P2P Federal Implementation Lead

Washington, DC +1 · Full-time · Posted Oct 29, 2026

Location
Washington, DC
Gaithersburg, Maryland, United States
Experience
8+ years of Procure-to-Pay and Oracle ERP Financials/Procurement experience.
Education
BS
Visa sponsorship
U.S. Citizenship or Lawful Permanent Residency with a minimum of three (3) years of U.S. residency.

About the job

Leads ground-up design and implementation of Oracle Procure-to-Pay for a multi-tenant federal financial management modernization program, including configuration, integrations, testing, controls, and AI-enabled delivery.

What you'll do

  • Lead P2P global design, requirements and fit-gap workshops, and key design decision analysis for a net-new Oracle implementation.
  • Design federal procurement, payables, payments, budgetary accounting, and system integrations; document functional specifications and controls.
  • Lead testing, defect resolution, conversion validation, deployment support, and use of AI tools while mentoring junior colleagues.

What they ask for

  • Substantial experience with federal P2P practices, including federal procurement and payables.
  • Lead implementer for P2P on at least one Oracle implementation; experience with ground-up solutioning and global design.
  • Strong knowledge of FAR, invoice processing, vendor payment workflows, and Oracle approval workflows/BPM.
  • Experience with FBDI, OTBI/BIP, interfaces and conversions; strong testing and troubleshooting; demonstrated use of AI tools.

Nice to have

  • Lead implementer experience on Oracle Fusion Cloud; Oracle Cloud is preferred over EBS-only experience.
  • Knowledge of FIBF, USSGL, federal disbursement/payment systems, G-Invoicing, OMB A-123, or federal ERP modernization.

Tools

Oracle Fusion Cloud ERP, Oracle EBS, FBDI, OTBI/BIP, BPM, Claude, ChatGPT, Copilot

AI focus

Use AI tools to accelerate fit-gap analysis, functional designs and specifications, test scenarios, defect troubleshooting, and configuration documentation; mentor junior colleagues in effective AI use.

Model-written summary · the original posting is authoritative

First seen Oct 30, 2026 · Last verified Nov 5, 2026 (UTC)