Software Engineer with a diverse range of experiences in various areas, including Machine Learning, Data Stream Processing, and Distributed Systems Design and Implementation. I thrive in roles and environments that require both technical and managerial skills. I am used to leading design and development of solutions using a data-driven, fail-fast approach.

Technical Program Manager @ I work in Google Cloud Dataflow, a Cloud-native data processing service. From September 2014 to Present (1 year 4 months) Senior Program Manager @ - StreamInsight: I was responsible for defining and directing the integration with Performance Counters and ETW, which shipped in StreamInsight 1.2. In partnership with the Cloud Programmability team, I lead design, implementation, and delivery of a complete overhaul of the surface API that embraced push- and pull-based collections (IEnumerable and IObservable) as well as a new IStreamable interface that represents temporally-punctuated streams to supersede the original adapter SDK. These changes also enabled the use of Reactive Extensions as part of a StreamInsight computation.The changes resulted in some of our customer’s codebases reduction in complexity and 50% (or more) LOCs removed. This value proposition shipped in StreamInsight 2.1. - Azure Customer Advisory Team (CAT): I worked with strategic customers adopting the Microsoft Azure platform. In CAT I had the opportunity to work with several Fortune 500 companies (and internal Microsoft properties) as a consulting architect. Most of the projects I touched benefitted from adopting reactive programming constructs and concepts in heterogeneous data processing environments, as well as tried-and-tested PaaS patterns that enabled high availability and scalability of their systems. I also had the opportunity to work with several internal technologies that address distributed systems programmability and design. I was also a developer, co-release manager, and product manager of the Cloud Service Fundamentals set of best practices. - Office365 Suite User Experience: I led the crew that optimized our internal big data analytics platform adoption, helping reduce daily batch job wall-clock runtimes by two orders of magnitude. I worked on redesigning some of our existing services, in one case increasing throughput of one of our main services by 42%. From September 2010 to September 2014 (4 years 1 month) Research Assistant @ While pursuing a Ph.D. in Computer Science, I worked primarily on two types of systems: - The NiagaraST Data Stream Management System. NiagaraST is a Data Stream Management System punctuations to evaluate continuous queries over unbounded streams of strongly-typed tuples. The NiagaraST algebra is very similar to Relational Algebra. In this system, I introduced the notion of data-descriptive feedback punctuations used to adapt running-query evaluation based on external stimuli, and also introduced a type system that described not only the types of tuples, but the types of punctuations produced by any subexpression. The latter type system can be used to statically determine whether a continuous query can eventually produce all output and cleanse all state given the expected placement and type of punctuations in the input stream(s). I also extended some basic functionality to simplify operator throughput analysis and profiling. - The Portland Oregon Regional Transportation Archive Listing (PORTAL). PORTAL is the Portland Metro Area’s designated traffic data repository, which is hosted and maintained by the Intelligent Transportation Systems Laboratory at Portland State University. While my doctoral studies focused on computer science and system design, I was fortunate enough to also be a part of the ITS lab and work on the various aspects of the PORTAL system, which included ingestion of telemetry data, querying, visualization and optimization. I also developed simple predictors and detectors of bottleneck formation based on the archived telemetry data. From 2006 to 2010 (4 years) Portland, Oregon AreaIntern @ Near the end of my doctoral residence, I interned at Microsoft working in a project then-known as “Orinoco”, later released under the name “StreamInsight”. StreamInsight is a temporally-punctuated Data Stream Processing System based on the CEDR algebra. I worked primarily on a declarative framework that enabled testing of the API surface, as well as other shipping considerations which included the ability to integrate to Microsoft’s Error Reporting infrastructure. Both features were part of the first public preview of StreamInsight. From June 2009 to September 2009 (4 months)

Doctor of Philosophy (Ph.D.), Computer Science @ Portland State University From 2006 to 2012 Master of Science (M.Sc.), Computer Science @ OGI School of Engineering at OHSU From 2003 to 2005 Bachelor of Science (B.Sc.), Computer Systems Engineering @ ITESM Campus San Luis Potosi From 1997 to 2001 Rafael Moctezuma is skilled in: Software Engineering, Distributed Systems, Machine Learning, LaTeX, Software Development, SQL, Java, Algorithms, C, C++, Data Mining, Big Data, Computer Science, Eclipse, JavaScript