Stefan Panayotov is a Senior Data Engineer at Acuity, Inc.. He possess expertise in oracle, xml, web services, microsoft sql server, spring and 49 more skills. He is proficient in Russian and English. Colleagues describe him as "I have worked with Stefan for the better part of a year. During this time I have come to appreciate the incredible depth of his knowledge base. To provide an example- Stefan's is currently the lead developer and architect of our spark big data platform. Underneath spark is a very complex abstraction for distributed asynchronous computation known as the actor model- in one of his prior positions programmed this abstraction in assembly. I am confident that, given the opportunity, Stefan could explain how spark worked from to bottom. From C#, to assembly, to scala Stefan can do it all and do it well. He knows the caveats associated with various approaches, he knows how to build machine learning pipelines, he knows how to manage massive data sets, design databases, he can write optimized code at any level of abstraction. Stefan is the guy who you go to with a question you can't find the answer to on stack overflow. Stefan is a world class software engineer." and "Over the short time I have known him, Stefan has proven himself to be an absolute expert in the fields of big data, object oriented and functional programming, and systems architecture. In his first few months at Cadent, Stefan single handedly designed, developed, and implemented a big data platform to support our data science and business intelligence teams. Over that time, Stefan compared multiple cloud hosting providers as well as an in house solution from a cost and performance perspective. During the integration and data migration phase, Stefan created scripts across multiple languages (C#, Python, and Scala to name a few) to manage the ETL processes and implement machine learning algorithms developed by our data science team. Given his experience, education, and work ethic, Stefan is capable of managing and or directly implementing large scale IT infrastructures within the data science and data engineering space. "