Daniel C.

Quant Data Engineer | Futures Tick Data, Polars Pipelines & ACSIL

Originally from Italy, I hold a Bachelor of Architecture (BArch) from the New York Institute of Technology - Manhattan. I developed my real estate career in Los Angeles and am currently operating from Warsaw. My work in real estate investment and project management continues to the present as an active, ongoing endeavor alongside my data engineering operations. I engineer the data foundations behind reliable futures research. From cleaned tick and Level 2 datasets to high-performance Python/Polars pipelines and native C++/ACSIL tools, I help traders, quantitative researchers, and systematic trading teams turn fragmented market data into reproducible research infrastructure. My approach is also shaped by an ongoing career in real-estate investment and project management that began in Los Angeles. Managing complex budgets, contractors, timelines, and execution risks taught me to work with clear scope, disciplined validation, transparent communication, and accountability from requirements through handover. In futures research, the most damaging problems are often hidden inside the data: incorrect contract transitions, inconsistent session boundaries, duplicate records, timestamp disorder, unexplained gaps, and transformations that cannot be reproduced. I build systems that make those risks visible, testable, and documented before they affect research conclusions. CORE CAPABILITIES Futures Data Engineering Tick ​​and Level 2 market-data normalization Contract-separated datasets and point-in-time lead-contract mapping Rollover logic, RTH/ETH session filtering, and custom schemas High-Performance Research Pipelines Python and Polars ETL Apache Parquet conversion, partitioning, and storage optimization Scalable processing, research loaders, and reproducible backtesting workflows Sierra Chart Engineering Custom C++/ACSIL studies and automation modules Data conversion, configuration, and research-infrastructure integration Validation and Delivery Duplicate-record and timestamp-monotonicity checks Schema manifests and explicit gap reports Versioned files, validation notes, and clearly documented limitations You will not receive an undocumented script or a folder of unexplained market data. You will receive a traceable, maintainable deliverable designed to be understood, tested, and used after handover. My work focuses on quantitative data engineering, research infrastructure, and trading-system implementation—not financial advice, trading signals, or guaranteed results. Send me your instrument, data source, date range, target environment, and acceptance criteria. I will outline what is feasible, what must be validated, and the most reliable path from raw market data to a validated research workflow.