IVAN
LEE
Senior Developer building at the intersection of data infrastructure and finance.
Designing high-performance ETL pipelines, distributed database systems, and trade risk platforms that support front-office quants and traders.
I build systems that process millions of trades daily, power real-time risk decisions, and turn raw market data into actionable signals.
Operating at the intersection of data engineering and quantitative finance. My work sits between the data and the decision — building the infrastructure that traders and quants depend on.
Previously built ML platforms at DBS Bank powering 100+ models, and NLP pipelines for credit sentiment used by prop traders to price distressed bonds.
- Launched AI initiative using Claude SDK + MCP, enabling natural language queries across internal data
- Scaled real-time PnL & risk systems processing ~3M trades/day via vectorized Pandas + multiprocessing
- Built ETL pipelines with Airflow & Jenkins transforming raw data into post-trade analytics
- Designed FastAPI risk transfer system with dry-run impact analysis and approval workflows
- Optimized MongoDB & ClickHouse with query guardrails to prevent DoS from expensive queries
Projects
04 itemsHigh-performance market data pipeline built in Rust for real-time trade data ingestion and processing.
Retrieval-Augmented Generation system using FAISS vector search and LangChain for document Q&A.
Framework for evaluating and benchmarking LLM outputs with structured metrics and comparison tools.
Automated README generator that analyzes repository structure and produces comprehensive documentation.