I build quantitative infrastructure that survives contact with production.
Background
My career spans both sides of the quant/engineering divide. I started in quantitative finance (risk modeling, market data systems, trading infrastructure) then spent over a decade building distributed systems and SaaS platforms at scale. Now I’m back in quant, bringing production engineering discipline to algorithmic trading.
This combination is rare. Most quants haven’t shipped production systems to millions of users. Most software engineers haven’t built risk models or market data pipelines. I’ve done both.
Early Career: Quantitative Finance (1998–2011)
BNP Paribas: Quantitative risk modeling, building the foundations of how I think about financial systems and statistical validation.
Bank of America: Interest rate product risk modeling, working with the complexities of fixed income derivatives and rate-sensitive portfolios.
Citadel: Data engineering focused on market data infrastructure, capital structures, and knowledge management systems for one of the world’s most sophisticated trading operations.
Stark Investments: Building financial infrastructure for market data ingestion and risk management systems.
Mid-Career: Distributed Systems & SaaS (2011–2025)
Salesforce (Desk.com): Stateful distributed services integrating multi-channel support workflows in Scala, Akka, and Ruby/Rails.
Jive Software / Lookout: Site reliability engineering and infrastructure automation with Nix, AWS, and Scala.
Daily Kos: High-availability infrastructure and scalable data analytics pipelines using Haskell, PureScript, and NixOS.
Northern Trust: VP Software Engineering leading teams building portfolio analytics, performance measurement, and risk management SaaS for institutional clients (pension funds, endowments). Scala/ZIO, TypeScript, React, AWS/Kubernetes.
Referential Labs: Principal Software Engineer delivering distributed data pipelines and platform services for clients including Caesars Digital and Capital One. Drove 27% AWS cost reduction while scaling ML workloads.
Get in Touch
I’m focused on quantitative development and research at Referential Labs. For collaboration inquiries or to discuss quant infrastructure challenges, reach out via LinkedIn .
Current Focus: Quantitative Systems (2025–Present)
Now at Referential Labs as Lead Quant Developer, I’m building infrastructure for validating algorithmic trading strategies:
Backtesting & Validation Pipelines: Robustness testing using quantitative, statistical, and econometric methods. QuestDB for time-series storage, Python ecosystem (pandas, numpy, polars, scipy, zipline-reloaded) for analysis.
Strategy Research: Applying property-based thinking to strategy validation. If a backtest passes, it should pass for the right reasons, not because of lookahead bias or overfitting.
Type-Driven Trading Systems: Leveraging Scala/ZIO and functional programming to build systems where entire classes of bugs are impossible by construction.
Technical Philosophy
Production systems fail in ways that backtests never predict. My approach:
Correctness first: Type systems and property-based tests catch bugs that no amount of manual testing will find.
Validate, don’t optimize: A strategy that’s robust to parameter variation beats one that’s overfit to historical quirks.
Ship to production: Research that never deploys is just expensive entertainment. Engineering discipline bridges the gap.
Tech Stack
- Quant: Python, pandas, polars, numpy, scipy, zipline-reloaded, QuestDB
- Systems: Scala 3, ZIO 2, Haskell, TypeScript
- Infrastructure: AWS, Kubernetes, NixOS, Terraform
- Data: Time-series pipelines, market data ingestion, analytics at scale
Publications
Author of the Git chapter in The Architecture of Open-Source Applications, Volume II (2012). Proceeds benefited Amnesty International.