Woobin JooResume (PDF, opens in a new tab)
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Woobin Joo

Vice President of Engineering Settlement Systems

New York, NY

Automating quality control and funding.

Engineering leader building financial systems that move money reliably.

I lead hands on engineering teams across payment authorization, settlement, funding, reconciliation, workflow automation, and distributed backend systems.

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Payment processor integrations
5+
Engineers and contractors led and mentored over time
10+
Engineering ownership
Architecture, implementation, and production.

Selected work

Financial infrastructure, made more reliable.
A closer look at the systems and teams behind it.

Automating financial operations

Reduced manual operational processing.

Replaced fragmented background jobs with coordinated workflows for quality control, funding execution, and exception handling.

I designed the architecture and led delivery using Temporal.

Explore the approach to Automating financial operations
Approach
Used Temporal to coordinate dependent services and retain workflow state, with controlled retries and recovery when processing failed. Operational visibility supported exception handling and manual intervention when needed.
Technologies
Temporal · Backend services · Observability

Protecting transaction integrity

More reliable, traceable payment investigations.

Connected authorization, settlement, investigation, and reconciliation through auditable payment services.

I architected the REST APIs and transaction processing services.

Technologies: REST APIs / SQL / Reconciliation

Explore the approach to Protecting transaction integrity
Situation
Financial systems need to know whether a payment was authorized, whether it has already been processed, how it settled, and how to investigate discrepancies.
Engineering challenge
Prevent duplicate processing while maintaining an auditable path across the transaction lifecycle.
Approach
Designed authorization services, idempotency, duplicate payment prevention, audit logging, and authorization to settlement matching to support accurate reconciliation and investigations.
Leadership dimension
Combined financial domain understanding with backend architecture to improve transaction correctness.

Scaling payment integrations

5+ payment processor integrations delivered.

Led processor integrations from ambiguous requirements through production ownership.

I owned architecture and implementation, coordinating with engineering, product, operations, and external partners.

Technologies: Microservices / REST APIs / AWS

Explore the approach to Scaling payment integrations
Situation
Payment infrastructure had to communicate reliably with multiple external financial partners.
Engineering challenge
Each processor brought different technical requirements, operational behaviors, settlement formats, and production concerns.
Approach
Established reliable integration patterns across payment processing, settlement, funding, and reconciliation, owning implementation through production deployment.
Leadership dimension
Led delivery across technical and operational stakeholders, from requirements to production lifecycle ownership.

Built through experience

From manufacturing operations to financial infrastructure. Staying close to the systems and the people building them.

  1. Dec 2019 — PresentCurrent role

    Vice President of Engineering

    Settlement Systems

    Boost Payment SolutionsNew York, NY

    Lead engineering across settlement, funding, authorization, reconciliation, and production reliability while staying closely involved in architecture and implementation.

    Selected contributions at Boost Payment Solutions
    • Led and mentored 10+ engineers and contractors across internal and external teams over time.
    • Delivered integrations with 5+ payment processors, owning architecture through production deployment.
    • Designed Temporal automation that reduced manual operational processing by approximately 75%.
    • Architected payment authorization, idempotency, reconciliation, and investigation services.
    • Built operational dashboards for transaction integrity, reconciliation exceptions, system health, and processing performance.
  2. May 2017 — 2019

    Assistant System Developer

    Automotive manufacturing systems

    Unify Systems · Hyundai MobisMontgomery, AL

    Built full stack features and backend services supporting automotive manufacturing operations.

    Selected contributions at Unify Systems · Hyundai Mobis
    • Built C# and ASP.NET services, SQL infrastructure, ETL pipelines, and real-time integrations.
    • Designed and optimized SQL jobs, backend data structures, and web services.
    • Maintained mission-critical integrations between SAP and MES systems across multiple vehicle programs.
  3. Jan 2016 — May 2017

    Data Analyst

    Systems analysis

    ARMA InternationalAlbany, NY

    Analyzed traffic systems and worked with engineers to evaluate signal timing, control software, and sensor performance.

    Selected contributions at ARMA International
    • Collaborated with traffic engineers to evaluate signal timing, control systems, and sensor performance.
    • Assessed traffic-control software and recommended improvements to signal efficiency and sensor deployment.

How I lead

Clear ownership, sound engineering decisions, and support for the people delivering them.

Public projects

Three pinned projects showing how I build beyond financial systems.

GitHub profile (opens in a new tab)

Quota

A web app for contractors to manage clients, reuse service prices, and create itemized quotes and PDF invoices. Saved quotes can be emailed and tracked with payment records.

Reusable pricing reduces repetitive entry, while one workflow keeps job estimates, client paperwork, and payment status together.

Built with: TypeScript · Next.js · PostgreSQL / Supabase · Auth0 · Stripe · Resend

Quoting and invoicing implemented. Voice-to-quote is planned.

View repository: Quota (opens in a new tab)
Project details: Quota
Problem it addresses
Turning a job description into professional paperwork involves pricing the work, preparing line items, and following up on payment. Quota connects these steps around the contractor’s own price book: choose a client, add work and quantities, save, then export or email the document and record payments.
How AI helps
Voice-to-quote is planned. A contractor could say, “Replace four outlets and allow six hours of labor.” AI would transcribe the recording, identify the work and quantities, and match them to the saved price book. It would prepare an editable draft using the contractor’s own prices. The contractor checks the customer, wording, quantities, and prices before saving or sending.
Engineering focus
Atomic quote creation keeps the quote, line items, and payment schedule together in one database transaction. Shared request budgets work across app instances, and billing revisions stop late events from overwriting newer subscription state.
Current status
Catalog-based quoting, PDF invoices, email, and payment records are implemented. Voice capture and AI drafting are planned. Client payments happen outside Quota; Stripe manages the contractor’s subscription.
Explore billing code and tests (opens in a new tab)
Quota dashboard showing fictional clients, paid and unpaid quotes, and payment summaries

Select an image to view it full size.

Current quoting interface with synthetic demo data.

Housekeeping for iOS

A native iPhone app for two household members to record purchases, attach private photos, and maintain a shared inventory. Search for an item, check its quantity, or see when it needs restocking.

Both people can see what’s already at home and update the same records, with restock indicators based on each item’s quantity and threshold.

Built with: Swift · SwiftUI · Supabase

Shared inventory implemented. Recognition uses sample responses; live recognition is disabled.

View repository: Housekeeping for iOS (opens in a new tab)
Project details: Housekeeping for iOS
Problem it addresses
Purchase photos alone do not show what a household owns or what needs replacing. The app connects each photo to a searchable purchase record, a quantity, and a restock threshold. Either household member can maintain the inventory, while access rules keep records and photos within the household.
How AI helps
The intended flow is photo → suggestions → review → save. AI would suggest a product’s name, description, brand, and model when identifiable—for example, turning a kettle photo into an “Electric kettle” draft. Users can edit suggestions or enter details manually, and nothing becomes a purchase record until they save. The recognition flow is tested with sample responses; live recognition is currently disabled.
Engineering focus
Account-scoped drafts preserve unfinished edits and keep data separate between sign-ins. Household access rules protect shared records and private photos. Photo preparation removes metadata before upload, with checkpoints that let interrupted saves recover.
Current status
Photo capture, manual entry, and shared storage are implemented. Recognition is tested with sample responses; hosted and device acceptance remain in progress.
Explore the architecture and native tests (opens in a new tab)
Offline iPhone inventory preview with search and sample household itemsOffline iPhone purchase-photo review with editable suggested product details

Select an image to view it full size.

Offline simulator previews with sample artwork and scripted suggestions. Live recognition is disabled.

Multi-agent Conversation

Two AI agents turn a conversation into a short, evolving story. Set the tone for each, introduce a plot twist, and guide the narrative as it unfolds.

Explore how character instructions and a shared conversation history shape a story, while directing the exchange without writing every line yourself.

Built with: TypeScript · React · NestJS · Groq · Server-Sent Events

Two characters, one model. Local demo; live generation requires a Groq key.

View repository: Multi-agent Conversation (opens in a new tab)
Project details: Multi-agent Conversation
Problem it addresses
The demo makes an AI exchange observable and steerable. Try an angry character opposite a friendly one, change their moods mid-conversation, or introduce a twist such as “A storm rolls in.” The interface gives the viewer control while the app coordinates the characters’ turns.
How AI helps
The two agents share one underlying language model but receive separate role and mood instructions. Each turn uses the conversation so far to generate the next reply; mood changes and story redirects guide subsequent turns. The app coordinates up to 15 turns and streams dialogue as it arrives. Live generation requires a configured Groq key; the screenshots use scripted sample dialogue.
Engineering focus
Owner checks protect each session’s controls. Abort signals cancel abandoned generation; deadlines, input limits, and request budgets bound provider calls. The backend streams events to the React interface without waiting for the whole exchange to finish.
Current status
A local demo with in-memory sessions. Public multi-user hosting would need additional authentication and shared spending controls.
Explore streaming and session controls (opens in a new tab)
Multi-agent conversation interface with character moods, sample dialogue, and story controls

Select an image to view it full size.

Real interface with scripted sample dialogue; local demo.

The technical foundation

Domain understanding and hands on engineering, from system boundaries to production behavior.

Financial systems

  • Payment authorization
  • Settlement & funding
  • Reconciliation
  • Processor integrations
  • Transaction integrity & auditability

Backend & platform

  • NestJS & REST APIs
  • Temporal & microservices
  • AWS
  • Docker & Kubernetes

Languages & data

  • JavaScript & Next.js
  • Python & Java
  • C# & ASP.NET Core
  • MySQL & PostgreSQL

Engineering practices

  • Architecture & code review
  • Mentorship & team execution
  • Specification driven development
  • AI assisted development & LLM integration

Education

State University of New York at Albany

B.S. in Information and Technology · Software Engineering

Build something that has to be right.

I am interested in engineering leadership roles where backend systems, financial correctness, workflow automation, and team execution matter.

woobin0413@gmail.comDownload resume PDFGitHub profile (opens in a new tab)