Projects: OLAP backend, ETL, ML recommendation systems, R&D, other internal projects
Achievements:
— created ML product recommendation pipeline that was crunching terrabytes of raw data and served up to 1k product recommendation requests per second with response time SLA < 20ms
— created Recommendation Engine, that managed 100+ user-defined hierarchical models and served up to 1k rps with model response time under 20ms
— partially designed and worked on Analytics pipeline, that provided ETL and OLAP capabilities for billions of events per day, with petabytes of historic data and real-time reporting capabilities
Technologies: java, scala, kotlin, python, spark, spark ML, vertx, kubernetes, docker, AWS, mysql, vertica, clickhouse, cassandra, kafka, etc.