Radhouane Maatoug 🇵🇸
About Radhouane Maatoug 🇵🇸
Radhouane Maatoug is a Senior Data Engineer with extensive experience in data engineering, cloud technologies, and quantitative analysis. He has worked for various organizations, including Trainline and SNCF Connect & Tech, and holds multiple master's degrees in quantitative finance and information technology.
Current Role at Trainline
Radhouane Maatoug serves as a Senior Data Engineer at Trainline, a position he has held since 2022. His work is based in London, England, and follows a hybrid work model. In this role, he focuses on leveraging data engineering practices to enhance the company's data infrastructure and analytics capabilities.
Previous Experience in Data Engineering
Before joining Trainline, Radhouane Maatoug worked at several notable organizations. He was a Senior Cloud DataOps professional at Decathlon International for eight months in 2020, and a Senior GCP Data Engineer at Veolia for five months in 2021. Additionally, he held the position of Data Engineer at STET from 2019 to 2020, contributing to various data engineering projects.
Background in Data Science and Quantitative Analysis
Radhouane Maatoug has a strong foundation in data science and quantitative analysis. He worked as a Data Scientist at Banque de France from 2017 to 2019 and as a Quantitative Analyst at Misys from 2014 to 2016. His experience also includes consulting roles at BNP Paribas and Société Générale, where he applied quantitative models and analysis techniques.
Educational Qualifications
Radhouane Maatoug holds multiple Master's degrees in relevant fields. He studied Information Technology at the National School of Computer Science and Applied Mathematics of Grenoble from 2008 to 2011. He furthered his studies in Statistics, Probability, and Finance at Université Paris Cité from 2011 to 2012, and he achieved a Master's degree in quantitative finance from Grenoble IAE in 2010-2011.
Technical Skills and Tools Utilized
Radhouane Maatoug possesses a diverse skill set in data engineering and cloud technologies. He has utilized Terraform for AWS resource deployment and scheduled ETL tasks using Airflow. His technical environment includes Python, AWS services, Kafka, and Docker, among others. He has also implemented monitoring solutions using Grafana and New Relic.