My passion is finding a balance between the most effective and maintainable code and timely business delivery. I pick up new technologies quickly, have strong debugging skills, and love to dig into unfamiliar systems to find solutions to issues.
I've been fortunate to work with many geographically and culturally diverse colleagues and thrive in teams of that nature.
Experience
Bank of New York Mellon
09/2019 – Present
Specialist Developer
- Technical lead for BNY's electronic FX credit checking systems and function as product owner, working directly with the credit and risk teams to design and prioritize solutions.
- Leveraged AI (Windsurf & Claude) to go from novice to proficient in Angular in order to complete delivery of a credit override system for risk team to address issues with counterparty credit lines. Backend uses a combined CRUD view of the latest data with an event sourced audit log.
- Since 2020, led credit checking system's growth from 1 to 4 distributed data centers, 5 to 9 credit check calculations, and 5 thousand checks per day to 15-20 thousand while maintaining single digit millisecond response times for at least 95% of requests.
- Lead developer on AI-powered (GPT, Pydantic, Langchain) chat bot which allows Foreign Exchange traders to query credit checking system using natural language. Bot turns manual tasks which took minutes into automated responses in seconds.
- Maintainer of internal code library used to retrieve holiday and rest day data for date calculations across FX.
- Ported quant trading team's XVA model from Pandas/Numpy Python app to Java for inclusion in pre-trade evaluation system.
- Stood up an isolated instance of credit checking application to provide dashboard-based insight into BNY's FX exposure to major financial institutions. Dashboard identified over-exposure to a particular counterparty within its first week of Production usage.
01/2016 – 08/2019
Lead Developer
- Part of team named one of BNY Mellon’s Best in Class for 2018.
- Primary developer on new trade booking system which supported a new pricing engine, allowing the bank to save several million dollars per year.