Wael Motamed

• Pilotage de la modernisation de la Data Platform sur GCP, incluant migration ELT, gouvernance, monitoring, CI/CD et optimisation des coûts. • Encadrement technique des pratiques dbt, BigQuery et GCP afin d’améliorer la scalabilité, la maintenabilité et la fiabilité des pipelines data. • Mise en place de standards d’industrialisation pour les traitements data : conventions de nommage, CI/CD, monitoring, tests et documentation. • Contribution à la stratégie FinOps Data avec réduction des coûts BigQuery et optimisation de l’usage des ressources cloud. • Collaboration transverse avec les équipes Data Engineering, Data Analytics, Data Science et IT pour fiabiliser les données critiques du groupe. Technologies : DBT, Python, BigQuery, Cloud Run Jobs, Cloud function, Bitbucket

- Integrated raw data collected from different sources into Data Warehousing BigQuery and generated Goldens Tables from it using Data Build Tool. - Developed Python modules to recover and ingest data . - Deployed the application to GCP using Azure DevOps Pipelines. - Orchestrated DAGs with Airflow and maintained ELT pipelines

- Developed Python module dedicated to ingest, transform and store Data issued from social media generated by Radarly API. - Deployed the application to Google Cloud Platform. - Integrated the module within the Customer Data Platform of LVMH. Technologies : BigQuery, Cloud Run, Cloud Build, Cloud Scheduler, Cloud Secret, AI Platform.

- Designed and developed Python Libraries to automate non regression tests from 5G technology - Assured the migration from python 2 to python 3 - Conducted Workshops and Coaching sessions about Python.

Optimization of user mobility in 5G networks using machine learning algorithms: -Generate and analyze user data -Developing models to predict user configuration in 5G networks using Recurrent Neuron Network (RNN) and Reinforcement Learning (RL) algorithms. -The work developed during this course was filed as a patent entitled: “METHOD AND APPARATUS FOR HANDOVER PARAMETERS OPTIMISATION IN 5G NETWORKS BASED ON MACHINE LEARNING”