Samuel Bresciani
About Samuel Bresciani
Samuel Bresciani is a Data Engineer with experience in data structures for batch and streaming flows, utilizing technologies such as Scala, Java, Python, and SQL. He holds degrees in Physics from the Università degli Studi di Padova and has worked in various roles related to data analysis and software development.
Current Role as Data Engineer at F2Informatica
Samuel Bresciani currently serves as a Data Engineer at F2Informatica | Consulenza Cloud, NoSQL, IoT in Milano, Lombardia, Italia. He has been in this position since 2023, where he focuses on data structures for batch and streaming flows. His work involves utilizing technologies such as HBase, Spark, NiFi, Hue, Flume, and Cloudera Manager. Samuel develops solutions in an Agile environment, applying his skills in statistical data analysis to enhance problem-solving frameworks.
Previous Experience in Data Engineering and Development
Prior to his current role, Samuel worked at F2Informatica as a Python R&D Developer in the GIS sector for six months in 2022. He also held positions at AGM SOLUTIONS as a Data Scientist from 2021 to 2022 and at GMI srl as a Researcher and Software Developer from 2020 to 2021. His experience includes satellite GIS data analysis from his time at Almaviva, contributing to his expertise in data engineering.
Educational Background in Physics
Samuel Bresciani studied at Università degli Studi di Padova, where he earned a Laurea L in Physics from 2013 to 2017, followed by a Laurea Magistrale LM in Physics with a focus on Material Physics from 2017 to 2020. His academic training included courses in Advanced Laboratory, Fluid Dynamics and Plasma Physics, Advanced Statistics with R, Physics of Complex Systems, Statistical Mechanics, Solid State Physics, and Nanotechnologies.
Technical Skills and Technologies Utilized
In his professional roles, Samuel has developed proficiency in various programming languages, including Scala, Java, Python, and SQL. He has hands-on experience with technologies such as HBase, Spark, NiFi, Hue, Flume, and Cloudera Manager. His skill set also includes statistical data analysis, which he applies to problem-solving frameworks, enhancing his contributions in data engineering and development.
Courses and Certifications in Data Science
After completing his formal education, Samuel pursued further training by completing a course on Data Science and Machine Learning with Python. This additional education has equipped him with the necessary skills to analyze data effectively and implement machine learning techniques in his projects.