Hi — I’m Pete, a Chicago-based software engineer with a strong background in high-performance systems, quantitative tooling, and data-intensive infrastructure. My work sits at the intersection of low-latency engineering, Python/C++ development, and quant research support, making me well-aligned with modern trading system requirements.

Over the past several years, I’ve focused on building tools and pipelines that help researchers, quants, and data teams operate more efficiently. My projects include:

• Latency-sensitive market data systems

I’ve designed and profiled Python-driven event loops, WebSocket tick handlers, and high-frequency ingestion layers. Early experiments with asyncio taught me how microseconds compound — shaping my approach to writing predictable, efficient code under load.

• High-throughput data pipelines

I’ve built end-to-end ingestion pipelines that process tens of gigabytes of tick-level data per day, moving from naïve storage formats to optimized columnar systems such as Parquet with compression. These pipelines now support ML-based features, backtesting engines, and real-time monitoring dashboards.

• Quant research tooling & simulation

A significant portion of my work centers on helping quant teams move faster:

• building experiment frameworks
• designing simulation helpers
• optimizing strategy prototypes
• creating reproducible environments for backtests
  I enjoy supporting research teams because it combines engineering precision with scientific iteration.

• Infrastructure engineering (Python & C++)

While Python is my primary language for research tooling and pipelines, I also work with C++ when performance and system-level control matter. I’ve built components such as:

• low-overhead data parsers
• memory-optimized collectors
• bridging layers between Python research workflow and C++ execution paths

This generalist approach makes me comfortable working across Quant, Data, and Infrastructure teams — acting as the technical connective tissue between them.

• Greenfield, 0→1 systems

Much of what I build starts as a blank file. I enjoy architecting systems from scratch, designing for scalability from day one, and iterating quickly with end-users (researchers, analysts, or data engineers). I’m comfortable owning a project from idea to production.

My Philosophy

Trading systems reward precision — not just in code, but in thought.

I treat every microsecond, every pipeline bottleneck, and every piece of infrastructure as part of the strategy. Whether I’m profiling a hot loop, optimizing a parser, or improving a simulation engine, I focus on correctness first, performance second, and clarity always.

I’m motivated by environments where:

• engineering decisions directly impact strategy performance
• collaboration with quants leads to measurable research improvements
• small optimizations create large competitive advantages
• ownership, autonomy, and technical depth are valued

I thrive in fast, research-driven teams where problems are complex, stakes are high, and results are quantifiable.

Education

DePaul University

Master’s Degree — Computer Software Engineering
2009 – 2011

DePaul University

Bachelor’s Degree — Computer Science
2006 – 2009

National Louis University

Engineer’s Degree — Computer Science
2005 – 2009