Aleksandr Krainov

• Designed ETL pipelines processing 10M–1B+ records using Spark and SQL • Improved query performance by ~30% via partitioning and join optimization • Built data validation frameworks ensuring high data quality across large datasets • Developed end-to-end pipelines (ingestion → transformation → delivery) • Defined scalable data architecture for high-volume processing

• Built ETL pipelines processing 10M–500M+ records from multiple data sources • Transformed healthcare data into OMOP CDM (millions of patient records) • Improved pipeline performance by 20–40% through optimization techniques • Processed real-world datasets (Optum, HealthVerity, EPIC, Cerner and JMDC) ensuring data consistency • Collaborated with clients to deliver data solutions for research and analytics

• Designed ETL pipelines handling 10M–200M records per batch • Improved performance of data jobs by ~30% through SQL optimization • Built scalable data transformation workflows and ensured data integrity • Delivered data solutions in collaboration with cross-functional teams

• Developed database solutions and ETL processes for large datasets (millions of records) • Built and optimized PL/SQL procedures, functions, and data pipelines • Improved database performance (~20%) and resolved production issues • Supported data migration (MySQL → Oracle) and distributed systems