Staff Software Engineer - ERP & Financial Systems

Staff Engineer, ERP & Financial Systems AI-First · Oracle Fusion ERP & SCM · Individual Contributor AI-First Role: This role is expected to use AI coding assistants and analysis tools as a daily practice — not occasionally. You will also be the team's bar-setter for what responsible, high-quality AI-augmented engineering looks like in a financial systems context. About the Role We are looking for an AI-first Staff Engineer to own the technical architecture and delivery of complex integrations across Oracle Fusion ERP and SCM applications. This is a senior individual contributor role — the technical authority on integration design, platform resilience, and AI-augmented engineering practices for our most critical financial and operational flows: O2C, P2P, Inventory, and Costing. You will use AI tools to design faster, debug smarter, and raise the engineering bar across the team. You will also evaluate where Oracle AI and OCI AI services can be embedded into integration patterns — moving the team from reactive to predictive. Key Responsibilities Integration Architecture & Delivery • Lead the design and delivery of complex Oracle Fusion integrations and extensions across Finance and SCM — with AI-assisted design, code generation, and review embedded throughout the delivery lifecycle • Own end-to-end technical solutions for high-volume business flows (O2C, P2P, Inventory, Costing), leveraging AI tools to accelerate development and improve solution quality • Define and enforce integration standards, error-handling patterns, and reconciliation frameworks; include AI-generated code standards and review checkpoints in team practices • Design integrations that are observable by default — structured logging, alerting, and AI-assisted anomaly detection to reduce mean time to detect and resolve Platform Stability & Resilience • Ensure platform stability and performance during peak-season and financial close cycles; use AI-powered log analysis and monitoring to anticipate issues before they surface • Act as the technical escalation point for critical production incidents, leading AI-assisted root cause analysis — using pattern recognition and log summarization tools to compress RCA timelines • Proactively identify technical debt and systemic fragility; propose and execute remediation with a bias toward AI-augmentable solutions over pure manual effort AI-Augmented Engineering Practice • Identify and ship AI improvements: evaluate integration workflows for AI applicability — from Copilot-assisted code generation to AI-driven reconciliation and exception detection — and implement with appropriate guardrails • Evaluate Oracle and OCI AI capabilities: evaluate OCI AI Services (anomaly detection, forecasting, document understanding) for embedding within integration pipelines, and make go/no-go recommendations with clear ROI framing • Set AI engineering standards: define prompt engineering standards, AI code review criteria, and responsible-use guardrails for AI-generated integration code in a SOX-compliant environment • Track and report AI impact: measure and report the productivity and quality impact of AI tool adoption within the integration team — delivery speed, defect rate, RCA time Technical Leadership & Cross-Team Influence • Lead technical objectives for project implementations; own architecture decisions from design through production with a clear bias for simplicity, resilience, and AI-readiness • Identify training needs within the team, including AI tool onboarding — GitHub Copilot, OCI AI, ChatGPT/Claude for technical workflows — and deliver or coordinate upskilling • Identify, communicate, and mitigate technical and project risks early; flag AI-specific risks (hallucinated code, data leakage, model drift) alongside traditional integration risks • Ensure SOX compliance across all owned integrations and AI-augmented workflows; define controls and audit evidence requirements for AI-assisted processes Skills, Tools & Experience Oracle Fusion Technical Stack • Deep hands-on experience with Oracle Fusion: BIP, OTBI, FBDI, REST/SOAP APIs, ESS jobs, and HCM/ERP data models across Finance and SCM modules • Strong expertise in integrations and middleware: Oracle Integration Cloud (OIC), OCI services, event-driven and batch patterns, async processing, retry and idempotency design • Advanced SQL / PL-SQL skills; strong ERP data model knowledge across O2C, P2P, Inventory, and Costing flows • Experience designing for high-volume, high-availability ERP environments including peak-season and financial close scenarios • 8–12+ years of ERP technical experience, preferably in retail, eCommerce, or consumer products AI Fluency • Hands-on AI user: daily practitioner — GitHub Copilot, ChatGPT / Claude, or equivalent AI coding assistants as a core part of integration development and debugging • AI-assisted operations: experience using AI tools for log analysis, RCA compression, and anomaly pattern identification in complex integration environments • OCI AI / Oracle AI awareness: practical knowledge of OCI AI Services (Anomaly Detection, Document Understanding, Language) and where they add value within ERP integration pipelines • Prompt engineering: able to write and evaluate prompts for code generation, spec drafting, and technical documentation; understands quality and reliability constraints of AI-generated code • AI risk awareness: understands AI-specific risk in financial systems: hallucinated logic, model drift, data privacy, and audit trail requirements for AI-assisted processes Leadership & Organizational Skills • Technical authority who influences without authority — able to drive engineering standards across teams through example, documentation, and peer review • Detail-oriented with exceptional project implementation and process optimization skills; navigates ambiguity without losing precision • Highly effective communicator across technical and non-technical audiences — translates complex integration and AI concepts into business-relevant language • Strong prioritization and troubleshooting instincts; methodical and decisive under pressure in high-stakes production environments • Naturally collaborative across functions, time zones, and organizational layers; elevates the engineers around them • Thrives in fast-paced, dynamic environments where the technical landscape is evolving rapidly What Good Looks Like in 12 Months • AI tools are embedded in the team's integration delivery workflow with documented, measurable productivity and quality improvements • At least one AI-native integration capability is in production — e.g., OCI Anomaly Detection in a critical pipeline, AI-assisted reconciliation, or Copilot-accelerated FBDI automation • Integration standards documentation includes AI code review criteria and responsible-use guardrails for SOX-compliant environments • RCA timelines for major incidents are materially shorter due to AI-assisted log analysis and pattern detection • Two or more engineers on the team have meaningfully leveled up their AI tool fluency under your direct influence