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 DallasβFort Worth, TX Β· Open to remote
Languages
Backend & Frameworks
Frontend & Mobile
Data & Pipelines
Databases
Cloud & DevOps
π¦ Candilize β Distributed Market Data Platform
Java Β· Spring Boot Β· Kafka Β· gRPC Β· MongoDB Β· Redis Β· 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.
π§© Spring Boot Enterprise Patterns β 16 Design Patterns in Production Context
Java Β· Spring Boot 3 Β· PostgreSQL Β· JPA Β· 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.
β‘ Crypto OMS on AWS β Cloud-Native Order Management System
C# Β· .NET Core Β· AWS EKS Β· MSK (Kafka) Β· Terraform Β· 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.
π€ FM Analyzer Bot β AI-Powered Trading Intelligence
Python Β· ClickHouse Β· VectorBT Β· ML Β· Telegram Bot
AI-powered Telegram bot for crypto trading β built with a MEXC data pipeline, ClickHouse data warehouse, VectorBT backtesting engine, ML-based predictions, and agentic AI capabilities.
Next.js Β· TypeScript Β· 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 β Distributed trading backend with Actor model (Proto.Actor), sub-100ms execution for institutional clients, compliance checks, and portfolio risk calculations
- ProTradingScans β 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 β Real-time alerting system integrating with Finnhub, Polygon, and Norgate APIs β maintained 99.9% uptime
- Incometrader & Ivy Dividends β 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 β Python + Airflow + dbt pipelines transforming financial data from multiple APIs into PostgreSQL for analytics dashboards and backtesting
Fintech & Trading Systems ββββββββββββββββββββ 12+ years
Microservices Architecture ββββββββββββββββββββ Production scale
Event-Driven Systems (Kafka) ββββββββββββββββββββ Real-time pipelines
Data Pipelines (ETL/ELT) ββββββββββββββββββββ Airflow + dbt + Python
Mobile (React Native) ββββββββββββββββββββ Published apps (iOS + Android)
Cloud Infrastructure (AWS) ββββββββββββββββββββ EKS, RDS, S3, MSK
I care about building systems that actually hold up in production β 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 β from database schema design to Kafka consumer group tuning.
I'm always up for conversations about distributed systems, fintech architecture, or backend engineering challenges.

