Software & GTM Engineer · New York

Joseph Taddeo

I build the software and data systems behind business decisions.

Currently the sole engineer at MoFin Lending, a business-purpose real-estate lender. I turn borrower data, sales activity, and financial workflows into tools that teams can act on—from identifying financing demand to understanding a deal’s history.

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01

About

Joseph Taddeo

I work directly with founders, sales teams, engineers, and vendors to define the problem, evaluate services, and build the solution. My work spans data modeling, application development, integrations, infrastructure, and one-on-one support for the people using it.

Previously, I was the sole engineer at LuxeSwap, a luxury consignment business generating $2.5M+ annually. My acquisition, inventory, pricing, and customer-operations systems contributed to $350K+ in year-over-year revenue growth; listing automation cut preparation time by 95%.

Education

University at Albany, State University of New York

Bachelor of Science in Computer Science, Minor in Mathematics

August 2020 – May 2025

Capstone: Career Intelligence Platform (IBM Watson sponsor)

By the Numbers

100Ks

Borrowers processed at MoFin

0 → 1

Sole engineering ownership at two businesses

95%

Less listing preparation time at LuxeSwap

$350K+

LuxeSwap YoY revenue growth contributed to

Technical Skills

Languages

PythonJavaScriptTypeScriptSQLHTML/CSSJavaC

AI Workflows

ClaudeClaude Code / CoworkLLM APIsMCP IntegrationsSemantic AnalysisWorkflow Automation

Frontend

ReactNext.jsViteCSS Modulesi18nPlotlyMapbox

Backend

DjangoDjango NinjaNode.jsExpressREST APIsJWT Authentication

Data Engineering

ETL PipelinesPandasData ModelingEntity ResolutionLifecycle AttributionData ReconciliationWeb Scraping

Databases

Supabase (Postgres)SQLiteMongoDBRedis

Infrastructure

AWSTerraformDockerLinuxCloudflareVercelGitHubPostfixDovecotOpenDKIM

GTM Integrations

PipedriveAirtableClayZapierAircallPlusVibeSmartlead

Quality & Delivery

pytestPlaywrightVitestRuffmypySentryCI/CD
02

Projects

Lending intelligence, deal attribution, acquisition infrastructure, and commerce software. The business problem behind each build.

MoFin · Commercial data infrastructure

Deal Lifecycle & Attribution

Built a centralized view of a deal’s path across data providers, lead-list generation, outreach agencies and sequencers, CRM, communications, and internal processing and underwriting records. Reconstructed historical journeys to connect participants and touchpoints with stage outcomes.

↗ Made fragmented deal histories traceable and usable for evaluation across teams

Data ModelingEntity ResolutionPipedrivePlusVibe / SmartleadSemantic Analysis
  • Historical deal reconstruction at scale
  • Connected email, call, text, CRM, and underwriting records
  • Stage-level signals for engineering, sales, and borrower evaluation
How I built it
  • Linked identifiable people and interactions across services
  • Combined structured records with semantic and statistical analysis where needed
  • Centralized record keeping and visibility into the deal lifecycle

MoFin · Proprietary analytics platform

Borrower & Lending Intelligence

Combined SFRA and Forecasa records with Zillow market data, FRED mortgage-rate history, and internal borrower and loan-performance datasets. Built the processing and analysis that turn these sources into borrower-fit and financing-demand signals.

↗ Gave sales a basis for identifying whom to contact, why, and when

PythonSQLPostgreSQLETLEntity ResolutionWeb Scraping
  • Hundreds of thousands of borrowers processed
  • External market data joined with internal historical performance
  • Product rules and transaction patterns translated into outreach priorities
How I built it
  • Resolved related borrower entities
  • Reconstructed mortgage and transaction histories
  • Built models of borrower fit and financing demand

MoFin · Infrastructure and service integrations

Acquisition Infrastructure & Workflows

Built proprietary outbound infrastructure from zero and connected enrichment, public-data processing, CRM records, and campaign delivery through internal codebases and server-side pipelines.

↗ Created an owned foundation for scaling prospect research and outreach

LinuxPostfixCloudflareClayAirtableZapierPipedrive
  • Scaled from zero to hundreds of mailboxes
  • Capacity to contact hundreds of thousands of leads monthly
  • Resumable processing with deduplication and state tracking
How I built it
  • Automated mailbox provisioning and deliverability monitoring
  • Connected custom processing with business-facing tools
  • Turned manual research and handoffs into repeatable workflows

MoFin · Web development and quality engineering

Lending Website & Financial Tools

Rebuilt the company’s Webflow site in Next.js and React, implementing financial calculators and validated lead capture. Added automated checks to preserve lending calculations, content, and interactions as the site changes.

↗ Built a maintainable customer-facing foundation for lending inquiries

Next.jsReactTypeScriptAirtablePlaywrightVitest
  • Financial calculators with testable business logic
  • Validated lead capture connected to Airtable
  • Automated content, visual, and interaction checks
How I built it
  • Translated lending requirements into customer-facing tools
  • Built regression checks for the migration
  • Connected lead intake to the broader operations workflow
LuxeSwap Web Platform screenshot

Full-stack production platform

LuxeSwap Web Platform

Built the company's entire web platform from zero in 6 weeks. The company had no website before this. Full-stack production system with live inventory integration, B2B lead generation, and 8-language internationalization.

↗ Part of the LuxeSwap software suite that contributed to $350K+ year-over-year revenue growth

React 19ViteExpress 5Node.jsRedisSupabaseAWS S3
  • Live inventory integration and B2B acquisition
  • 19 pages with 8-language i18n (incl. RTL Arabic)
  • 50+ B2B leads generated monthly from zero prior pipeline
  • Built solo in 6 weeks
How I built it
  • Live eBay Browse API integration with Redis caching
  • "Artisan-Tier" brand showcases with scroll-driven CSS animations
  • Custom Puppeteer scraper for AuctionNinja with cron refresh
  • Dynamic sitemap, JSON-LD structured data, GDPR geo-based consent
View Live →

Internal business platform

Internal CRM & Analytics Dashboard

Built to replace 2,000+ manual spreadsheets. Full internal CRM with JWT-secured API, automated commission calculations, and dual customer segmentation — managing 750+ active consignor accounts.

↗ Replaced 2,000+ manual spreadsheets with unified system

React 19Express 5JWT AuthSupabase/PostgreSQLPython
  • 17 JWT-secured API endpoints
  • 750+ active accounts managed
  • $8.9M+ revenue and 150K+ items processed
  • Python ETL: 2,155 XLSX files → 25 canonical categories
How I built it
  • Dual customer segmentation (volume + value tier)
  • Batch lifecycle tracking with staged-save architecture
  • Automated commission calculations with full edit history
  • Inline editing with undo and audit trail
Brand Pricing Intelligence System screenshot

Data intelligence platform

Brand Pricing Intelligence System

Proprietary brand pricing index built from historical sales data. Cross-category median ratio computation with fuzzy matching and Levenshtein distance for brand deduplication across 1,021 brands.

↗ Powers real-time pricing decisions across 1,021 brands

Node.jsExpressSupabaseReact
  • 130K+ historical sales supporting the pricing engine
  • 1,021 brands classified into 4 tiers
  • 300+ brand aliases normalized
  • 9 analytics visualizations across 14 canonical categories
How I built it
  • Cross-category median ratio computation for tier assignment
  • Fuzzy matching + Levenshtein distance for deduplication
  • Real-time quote generation for consignment triage
  • Powers B2B outreach prioritization
EZ-Lister screenshot

AI-powered desktop application

EZ-Lister

AI-powered desktop app that automates eBay listing creation. Deployed as self-updating macOS app — CEO trained and operates independently in daily production use.

↗ Saves ~1.5–2 full-time employees of manual listing work

Pythonsentence-transformersTkinterPyInstaller
  • 26,000+ items processed annually
  • 95% time reduction (10 min → 30 sec per item)
  • $250K+ annual revenue enabled
  • ~1.5–2 FTE equivalent saved
How I built it
  • AI classification using sentence-transformer embeddings
  • 77+ categories via word-boundary regex matching
  • Self-updating macOS app with GitHub release detection
  • CEO operates independently — zero ongoing support needed
Sales Consolidator screenshot

Financial reconciliation tool

Sales Consolidator

Sales reconciliation pipeline matching eBay transaction reports to internal listing records, automating fee redistribution and consignment payout reporting.

PythonTkinterPandas
  • Processes 1,000+ transactions per batch
  • O(n+m) matching algorithm — 60s → <5s execution
How I built it
  • Unified Transaction Dictionary for O(1) lookups
  • Proportional fee redistribution for multi-item orders
  • HTML Meta-Index for batch metadata extraction
  • macOS app bundle for Gatekeeper compliance
Career Intelligence Platform screenshot

University capstone (IBM Watson sponsored)

Career Intelligence Platform

Led cross-functional team sponsored by IBM Watson researcher (Dr. Chidansh Bhatt). RAG-based career matching engine with semantic job-resume matching and geospatial salary visualizations.

PythonLangChainOpenAIMongoDBPlotlyMapbox
  • RAG-based career matching with OpenAI embeddings
  • 50-state salary choropleth visualizations
  • CareerOneStop (Dept. of Labor) API integration
How I built it
  • Semantic job-resume matching via OpenAI embeddings
  • GPT-powered resume and job listing parsers
  • Plotly choropleth salary maps across 50 states
  • Mapbox geographic analytics for applicant data
03

Experience

Roles where I've built and shipped production systems independently.

MoFin Lending Corporation

GTM & Operations Engineer

April 2026 – PresentNew York, NY

Business-purpose real-estate lending. Sole engineer partnering directly with the co-founder and sales leadership.

  • Built a proprietary lender-intelligence platform combining SFRA and Forecasa, Zillow market data, historical mortgage rates, and internal borrower and loan-performance records; processed hundreds of thousands of borrowers
  • Built deal attribution across lead sources, email sequencers and agencies, Pipedrive, email/call/text activity, and internal processing and underwriting records; reconstructed historical deal paths and linked participants, touchpoints, and stage outcomes
  • Modeled borrower fit and financing demand using entity resolution, mortgage reconstruction, transaction histories, product rules, and internal performance patterns
  • Built internal codebases and server-side pipelines connecting public-data collection with Pipedrive, Clay, Airtable, and Zapier; added deduplication, state tracking, and resumable processing
  • Built proprietary outbound infrastructure from zero across hundreds of mailboxes, with provisioning and deliverability monitoring and capacity to contact hundreds of thousands of leads monthly
  • Developed Django services, a Next.js website with financial calculators and lead capture, automated quality checks, and infrastructure as code
  • Led technical discovery with founders, sales, engineers, and vendors; integrated AI services and MCP-connected tools and worked one-on-one with sales to turn requirements into usable deliverables

LuxeSwap

Software Engineer

February 2025 – April 2026Oyster Bay, NY

Luxury menswear consignment house. 25+ years operating. 9-person team. Sole engineer.

  • Owned acquisition, inventory, pricing, and customer-operations software for a $2.5M+ annual business; systems contributed to $350K+ in year-over-year revenue growth
  • Built full-stack web platform from zero in 6 weeks, generating 50+ B2B leads monthly from no prior pipeline
  • Developed internal CRM with 17 JWT-secured API endpoints, replacing 2,000+ manual spreadsheets and managing 750+ consignor accounts
  • Built a pricing engine from 130K+ historical sales, supporting valuations and an interactive quote builder
  • Cut listing preparation 95%, from 10 minutes to 30 seconds per item, enabling $250K+ in annual revenue through added processing capacity
  • Unified 150K+ records across data sources via Python ETL pipeline normalizing 2,155 XLSX files into 25 canonical categories

FedEx Ground

Package Handler

May 2024 – February 2025Troy, NY

Part-time during final year of CS degree.

  • Processed 800–1,000+ packages per shift in high-throughput logistics environment
  • Maintained accuracy and met daily targets under tight operational deadlines
04

Contact

Interested in working together? Let's talk.

Direct Contact

jjtjoseph1@gmail.com516-669-9372

Connect

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