Forward Deployed Engineer

About the Role

Our client is modernizing its enterprise systems and building AI-native operations to support approximately $1 billion in annual revenue. Operations span wholesale and retail, online and offline channels, B2B, B2C and D2C, and multiple marketplace storefronts. In this hands-on role you will lead the technical delivery of system modernization, from legacy assessment through migration, cutover and continuous improvement, and you will design, deploy and continually evolve the AI-driven workflows embedded in how the business runs. Sitting within IT and partnering with Finance, Sales, e-commerce, supply chain and a China-based development team, you will translate business priorities into reliable, scalable technology. This is a unique opportunity to have an outsized impact in a highly visible and strategic area of the company.

Responsibilities

Enterprise Systems Modernization

• Assess and architect the transition: Inventory legacy applications, data structures, interfaces, scheduled jobs and dependencies. Support platform selection through technical requirements, scripted vendor demonstrations and fit-gap analysis covering APIs, data access, interoperability, security, scalability and support. Define the target integration architecture and a staged legacy-to-new transition plan.
• Implement integrations: Build connections between core enterprise platforms and order, inventory, WMS/3PL, retailer EDI, marketplace, D2C, settlement and BI systems. Define transaction identifiers, field mappings and data contracts, and enable controlled coexistence and parallel reconciliation without duplicate postings or lost transactions.
• Engineer and validate data migration: Build repeatable extract-transform-load tooling for configuration, master data, open transactions and balances using supported platform methods. Enforce load sequencing, mapping validation, duplicate prevention, exception handling and audit logging, and produce source-to-target reconciliations by record, quantity and value against Finance-approved definitions.
• Test and rehearse: Plan and run mock conversions and unit, system-integration, performance and user-acceptance testing with production-representative data and volumes. Validate failure, retry and recovery behavior, maintain test evidence traceable to requirements, and confirm that migration duration fits the cutover window.
• Lead cutover and stabilization: Own the technical cutover runbook: backups, data freeze, final and delta loads, interface switchover, validation checkpoints and rollback criteria. Provide readiness evidence for go/no-go decisions, execute approved deployment tasks, resolve hypercare issues and transition to steady-state support with documented ownership.
• Drive continuous improvement after go-live: Manage the enhancement backlog, release cycles, platform upgrades and new module rollouts, and adapt configuration and integrations as channels, entities and business models evolve.

AI-Native Workflows and Data Design

• AI-native workflows: Partner with process owners to rethink workflows from the business outcome back, eliminate steps that no longer add value, and embed AI agents and LLM-driven automation for extraction, classification, analysis, drafting and exception management, with human approval for material financial and customer decisions.
• Evolve workflows continuously: Treat every workflow as a living product: instrument accuracy, cycle time and adoption; run evaluation suites against verified cases; and iterate prompts, tools, models and business rules as business needs, data and AI capabilities change, through versioned and controlled releases.
• Architect for change: Maintain a modular, governed AI and integration architecture, including data-access services, tool and agent interfaces such as Model Context Protocol (MCP), and version-controlled prompts and skills, so that new use cases can be delivered and existing workflows reconfigured quickly as priorities shift.
• Establish trusted data: Implement shared definitions, master data, lineage, refresh schedules and quality controls; handle late, reversed and reprocessed records without omission or double counting, so that reporting, analytics and AI all rely on the same numbers.
• Operate with discipline: Apply code review, automated testing, least-privilege access, secrets management, change control, monitoring, alerting, incident response and manual fallback. Track operating cost and net efficiency, including human review time, and document and enable users, IT, the China-based development team and vendors to support what is delivered.

Requirements

Education and Experience

• A bachelor's or master's degree in computer science, information systems, data engineering or a related field, or equivalent professional experience.
• Typically three to eight years in enterprise systems integration, data engineering or business automation, with solutions running in production.

Enterprise Implementation Lifecycle

• Hands-on delivery on at least one full ERP or finance-system implementation, replacement or major upgrade across requirements and fit-gap, data migration and reconciliation, system-integration and user-acceptance testing, mock conversions, cutover and hypercare.
• Able to walk through your own technical deliverables and a real cutover or production issue.
• Reporting-only or end-user experience is not sufficient.

ERP and Finance Platforms

• Working experience with one or more of NetSuite, Microsoft Dynamics 365 (Finance & Operations or Business Central), SAP S/4HANA or Oracle Fusion Cloud ERP, including their native integration and migration tools (for example SuiteTalk and SuiteScript, the Data Management Framework, SAP Migration Cockpit or Oracle FBDI).
• Sound understanding of general ledger, AP/AR, inventory costing, order-to-cash, procure-to-pay, period close and financial controls.

Integration and Data Engineering

• Strong Python and SQL; REST and SOAP APIs, OAuth, webhooks and EDI (X12).
• An integration platform such as Celigo, Boomi, MuleSoft or Azure Integration Services.
• Relational databases and a cloud data platform such as Snowflake, BigQuery or Microsoft Fabric.
• Power BI or Tableau.

Production Engineering

• Git, CI/CD, automated testing and cloud deployment on Azure, AWS or Google Cloud.
• Monitoring and incident diagnosis; idempotent processing, safe replay and recovery design.

AI Engineering

• Hands-on with LLM APIs and enterprise AI platforms such as Anthropic Claude, OpenAI or Azure OpenAI, Google Gemini and Microsoft Copilot Studio.
• Agent and orchestration tooling such as MCP, LangGraph or LangChain, Power Automate or n8n.
• Retrieval-augmented generation, tool use, structured outputs, evaluation and guardrails.
• At least one AI workflow you built is in real use with measured accuracy and business value, and you can explain when to choose AI, deterministic rules or human review.

Business Understanding and Communication

• Translates business requirements into testable technical work and communicates clearly with Finance, business owners, vendors and US and China teams.
• Experience in inventory-based wholesale, retail or e-commerce and working proficiency in Mandarin Chinese are advantages.

Levels

• Forward Deployed Engineer: Typically three to five years of experience; has contributed technical deliverables to an ERP implementation or migration through testing and go-live, and built integrations and a tested AI workflow. Delivers assigned components under senior architecture and release review.
• Senior Forward Deployed Engineer: Typically five to eight years of experience; has led an ERP integration, data-migration or implementation workstream from technical design through cutover and stabilization, and taken an AI or automation workflow into production with measured results. Leads workstreams, mentors engineers and acts as technical lead with vendors.

Growth Path

Forward Deployed Engineer → Senior Forward Deployed Engineer → Business Systems & Automation Lead or Data & AI Engineering Lead → Director, Business Systems & AI.