Elena Romanova
About Elena Romanova
Elena Romanova is a Principal Data Architect at FINRA, where she has worked since 2005. She holds a Master's degree and a PhD from Lomonosov Moscow State University and has extensive experience in data architecture and analytics.
Work at FINRA
Elena Romanova has served as Principal Data Architect at FINRA since 2005, accumulating 19 years of experience in this role. Her responsibilities include overseeing the enterprise data architecture, particularly during the merger of NASD and NYSE member regulation. She has also prototyped a knowledge graph data transformation pipeline utilizing Spark DataFrame and GraphFrames libraries on AWS. Her work has significantly contributed to enhancing business analytic capabilities across various domains, including risk analysis and data integration.
Education and Expertise
Elena Romanova holds a Master's degree from Lomonosov Moscow State University (MSU) in the School of Mechanics and Mathematics. She further advanced her education by earning a Doctor of Philosophy (PhD) in Science from the same institution, specifically in the School of Geography. Her academic background provides a solid foundation for her expertise in data architecture and analytics.
Background
Before joining FINRA, Elena Romanova gained experience in various roles. She worked as a Senior Consultant at the Pension Benefit Guaranty Corporation (PBGC) from 1996 to 1999. Prior to that, she served as a Senior Developer at Wildlife International from 1993 to 1996. Her career began at Lomonosov Moscow State University, where she worked as a Scientist and Senior Data Analyst from 1986 to 1992. This diverse background has equipped her with a comprehensive understanding of data systems and analytics.
Achievements
Elena Romanova has made significant contributions to data architecture and analytics. She designed and built an Enterprise Knowledge Graph that simplifies data modeling, integration, and management, thereby revolutionizing Enterprise Data Strategy. Additionally, she has analyzed and implemented solutions for complex Big Query problems and created machine learning training data based on the knowledge graph to identify similarities between entities with heterogeneous features.