Miguel Duarte is a data-focused technology leader and founder with 12+ years of experience designing and delivering high-performance data pipelines, real-time fraud detection, cloud deployments and large-scale migrations for Telco, finance and government clients. As Director of Monaloxiot he combines hands-on engineering with client-facing delivery, emphasizing transparent, simple architectures and close stakeholder collaboration. He has a strong track record in performance engineering and real-time analytics from roles at Feedzai and Equal Experts, and has open-source contributions to the notable Vavr project improving test generators and benchmarking collection performance. Comfortable leading multicultural teams, he pairs technical depth (from mediation systems and telecom backbone work to ML scoring services) with practical product delivery. Notably, his background blends Carnegie Mellon software engineering training with decades of systems optimization that once achieved multi-hundred-percent performance gains on existing infrastructure.
12 years of coding experience
14 years of employment as a software developer
Licenciatura, Management and Informatics, Licenciatura, Management and Informatics at Iscte - Instituto Universitário de Lisboa
Colégio Manuel Bernardes
Master in Software Engineering, Software Engineering, Master in Software Engineering, Software Engineering at Carnegie Mellon University
vʌvr (formerly called Javaslang) is a non-commercial, non-profit object-functional library that runs with Java 8+. It aims to reduce the lines of code and increase code quality.
Role in this project:
Back-end Developer / QA Engineer
Contributions:8 commits, 4 PRs, 7 comments in 13 days
Contributions summary:Miguel contributed to the `vavr-io/vavr` project by adding features related to generating values from enums and iterables within the testing framework. They implemented generators for enums, arrays, and iterables. Additionally, the user fixed a typo, reverted some erroneous test code, and added `requireNonNull` checks for better error handling. Furthermore, they added a benchmark module to measure the performance of various Vavr collection operations.
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