Summary
Dimitri Kupov is a senior data engineer and analyst with over a decade of experience designing cloud-native data platforms, ETL pipelines, and ML-driven analytics across Azure, AWS, Databricks and Snowflake. He blends deep applied mathematics and creative machine-learning approaches—singular value decomposition, spectral clustering and Kalman filtering—to turn messy legacy transaction data into auditable, production-ready analytics and forensic accounting solutions. At Burns & McDonnell and prior roles he built end-to-end pipelines, AutoML concepts and LLM embeddings that surface audit flags and answer domain-specific queries for project managers and auditors. A pragmatic leader and agile project manager, he has repeatedly accelerated month-end close, improved revenue predictability, and scaled multi-vendor migrations while coaching cross-functional teams. Based in Greater Boston, he pairs formal training from MGIMO, Hult and MIT with a hands-on engineering mindset—by day and by night—delivering measurable business impact from data to decision.
10 years of coding experience
6 years of employment as a software developer
Master of Business Administration - MBA, Managerial Finance, Project Management, Data Analysis, Master of Business Administration - MBA, Managerial Finance, Project Management, Data Analysis at Hult Ashridge
Postgraduate Degree, Data Science, Data Modeling, Postgraduate Degree, Data Science, Data Modeling at Massachusetts Institute of Technology
Master's degree, MS in Applied, Computational Mathematics & Economics, Master's degree, MS in Applied, Computational Mathematics & Economics at Moscow State Institute of International Relations (University) MFA Russia MGIMO
Russian, English, German, Ukrainian