Senior Full-Stack Engineer with 12+ years building trading platforms, fintech systems, and SaaS applications.
I spend most of my time in Java Spring Boot and React on the application side, and Python + Airflow + dbt on the data pipeline side. I've designed systems that handle sub-100ms order execution, process 87K+ market symbols in real-time, and serve thousands of active mobile users.
Based in Dallas–Fort Worth, TX · Open to remote
Languages
Backend & Frameworks
Frontend & Mobile
Data & Pipelines
Databases
Cloud & DevOps
🏦 Candilize — Distributed Market Data Platform
Java · Spring Boot · Kafka · gRPC · MongoDB · Redis · Docker
Microservices system that fetches and serves OHLCV candle data from multiple crypto exchanges. Uses Kafka for async download pipelines, gRPC for inter-service communication, Redis caching, JWT auth, and Flyway migrations. Includes architecture diagrams and full API documentation.
🧩 Spring Boot Enterprise Patterns — 16 Design Patterns in Production Context
Java · Spring Boot 3 · PostgreSQL · JPA · SOLID
A reference implementation of 16 enterprise design patterns (Strategy, Observer, Chain of Responsibility, Saga, etc.) built into a multi-tenant order and audit platform. Demonstrates SOLID principles, clean architecture, and Java best practices.
⚡ Crypto OMS on AWS — Cloud-Native Order Management System
C# · .NET Core · AWS EKS · MSK (Kafka) · Terraform · DynamoDB
Enterprise-scale Order Management System designed for high-frequency crypto trading with sub-100ms latency. Deployed on AWS using EKS, MSK, and infrastructure-as-code with Terraform.
🤖 FM Analyzer Bot — AI-Powered Trading Intelligence
Python · ClickHouse · VectorBT · ML · Telegram Bot
AI-powered Telegram bot for crypto trading — built with a MEXC data pipeline, ClickHouse data warehouse, VectorBT backtesting engine, ML-based predictions, and agentic AI capabilities.
Next.js · TypeScript · Real-Time Data
Interactive financial dashboard rendering real-time market data with charting, watchlists, and portfolio analytics.
These aren't on GitHub (proprietary), but they shape how I think about engineering:
- EC Trading Platform — Distributed trading backend with Actor model (Proto.Actor), sub-100ms execution for institutional clients, compliance checks, and portfolio risk calculations
- ProTradingScans — Multi-threaded stock scanning engine covering 87K+ US and Australian symbols, 30+ filter criteria, 10+ technical indicators, MongoDB time-series optimization (45% faster queries)
- MarketAlertPro — Real-time alerting system integrating with Finnhub, Polygon, and Norgate APIs — maintained 99.9% uptime
- Incometrader & Ivy Dividends — React Native mobile apps shipped to App Store and Google Play, serving 5,000+ active users with real-time market data and subscription management
- ETL Pipelines — Python + Airflow + dbt pipelines transforming financial data from multiple APIs into PostgreSQL for analytics dashboards and backtesting
Fintech & Trading Systems ████████████████████ 12+ years
Microservices Architecture ██████████████████░░ Production scale
Event-Driven Systems (Kafka) ████████████████░░░░ Real-time pipelines
Data Pipelines (ETL/ELT) ██████████████░░░░░░ Airflow + dbt + Python
Mobile (React Native) ████████████░░░░░░░░ Published apps (iOS + Android)
Cloud Infrastructure (AWS) ████████████░░░░░░░░ EKS, RDS, S3, MSK
I care about building systems that actually hold up in production — not just pass a demo. That means thinking about retry logic before the happy path, understanding where your system will break at 10x load, and writing code that the next engineer can read without a Rosetta Stone.
Most of my career has been spent in fintech, where latency matters, data accuracy is non-negotiable, and downtime costs real money. That context shaped how I approach everything — from database schema design to Kafka consumer group tuning.
I'm always up for conversations about distributed systems, fintech architecture, or backend engineering challenges.