I am a Data Engineer who loves to experiment, tinker, and optimize every data flow until it runs deterministically. I build custom pipelines, quantitative calculation engines, and agentic workflows. High technical ambition, dry humor, zero fluff.
Selected Work
Market Analytics & Financial Strategy Backtesting Pipeline
A collaborative financial engineering platform to analyze FX rates, develop quantitative trading strategies, execute historical backtests, and monitor live volatility risks.
Collaborative Side-Quest · Financial Data & Strategy Analytics
High-Speed Quantitative Arbitrage & Odds Engine
A quantitative calculation engine designed to capture inefficient market spreads across sports betting markets through automated odds synchronization and risk-managed execution.
Quantitative Developer & Pipeline Architect · Private Repository
Multi-Agent System for Heavy Refactoring Workloads
A custom terminal-driven agentic orchestration framework built to execute multi-file repository refactorings with RunPod cloud GPU or local LLM compute offloading.
Autonomous Agent Framework · Heavy Refactoring Engine
Data Engineering Proof of Work & Fundamentals Lab
A personal research repository implementing numerical algorithms, classical AI search, RAG vector retrieval, and data architecture patterns from scratch.
Personal Research & Proof of Work · Deep Fundamentals
Multi-Database Workflow Automation Pipeline
An Electron desktop app and background sync utility aggregating fragmented Notion module databases into a single operational master table.
Automation Engineer · System Tray Utility
System Monitoring, Analysis & Resource Tracking
A collaborative Linux system telemetry suite built in Go & TypeScript to monitor kernel memory, CPU load, and systemd daemons in real time with AWS cloud integration.
Collaborative University Project · Personal Focus: AWS Infrastructure & Docker Integration
Engineering Discipline
Simple tools and clear algorithms solve most problems best. You don't need heavy cloud infrastructure everywhere when a well-configured server does the job faster and cheaper.
Instead of diving in blindly without a roadmap, build a clear, agile staged plan. Iterate systematically from core data schemas to full pipeline resilience.
Build an end-to-end working prototype first instead of endlessly over-refining components in isolation. Real integration feedback reveals what tests can never predict.
AI agents accelerate workflows, but my golden rule applies: Never let AI handle code you don't fully understand yourself. It saves money, time, and nerve-wracking debugging hours.
Technical Ecosystem
Cleanly categorized across system levels with subtle background cards and full brand saturation on hover.