Jakub Kiełbasiewicz

Designed and built a high-performance trading system architecture in Python Rust, achieving <30ms latency at the 0.5 quantile, enabling alpha capture in volatile markets. Researched and backtested Medium Frequency Trading (MFT) strategies, driving measurable improvements in execution quality and Sharpe ratio.

Developed and optimized a high-performance data model integrated with Palantir Foundry, managing billions of transactions and millions of addresses across multiple blockchains. Designed a compliance-focused staking solution with full PnL reporting, ensuring regulatory transparency and improving financial oversight.

Elevated data quality for DeFi quant research, optimizing storage and compute efficiency, resulting in substantial cost savings through improvements in memory engines, file formats, and indexing. Developed a unified data engine, standardizing data sources for research, significantly reducing the cycle time from idea generation to backtesting.

Led numerous projects for US-based companies, designing and implementing data warehouses and modern data environments. Ensured the highest data quality of the core product.

Architected foundational database components to support high-performance SQL and scalable data systems. Developed distributed algorithms, including consensus protocols and P2P networking, to enhance system reliability and operational efficiency for data-intensive applications.

Upgraded exchange connectors by integrating websockets and asynchronous programming, improving real-time data processing and responsiveness. Optimized data-intensive tasks, including enhancements in indexing and queue management, reducing data access and processing time significantly.

Streamlined Databricks implementation and optimized Spark processing for approximately 5TB/day pipelines. Implemented robust data quality monitoring solutions.

Migrated the entire data team (200+ people) to Databricks and optimized Spark performance, decreasing monthly compute costs by more than 50%. Re-designed storage for daily operations, which reduced processing and querying time.

Designed ETL processes for IoT devices. Implemented pipelines and aggregations that allowed running experiments on top of the stored data.

Led data integration between Laudamotion and Ryanair, and automated ETL pipelines. Prepared and ran PoC solutions in Azure (Databricks, ADF, PowerBI).

Designed and implemented MicroStrategy reporting solutions for a large telecom company. Resolved ETL issues and maintained ETL pipelines.

Pursuing a Master of Science in Applied Computer Science. The master's thesis involves analyzing market dynamics and time series dependencies for statistical arbitrage in decentralized finance.

Obtained a Bachelor of Arts in Applied Computer Science. The studies included a focus on quality testing of large production data sets.

Completed a Bachelor of Arts in Economics with a specialization in Econometrics. The thesis focused on an assessment of the privatization process in Poland between 1989 and 2015.