Data Cloud Engineer - AMADEUS - Nice, France
(2024-10)
Contrat de Professionalization
- Migrated C++ based ETL pipelines into Azure Databricks platform reducing processing time by 40%. Deployed UAT and PDT environment for Azure Databricks enabling stable pre-production validation.
- Deployed recovery pipelines or main BI projects from Scala to PySpark, improving data availability.
- Developed an Azure monitoring dashboard to track PNR ingestion and new data availability, improving ETL observability by reducing manual efforts by 70%.
- Delivered production-grade cost assessment for Qlik Sense Vs Power BI evaluating total cost ownership and integration feasibility within Amadeus's internal architecture.
- Deployed secured Azure network infrastructure, subnets and production-ready VMs hosting Power BI Gateway to enable enterprise-grade data exchange between internal system and Power BI Service.
Data & AI Engineer - Greater Paris University Hospitals - APHP - Paris, France
(2024-03 - 2024-10)
- Conducted literature review on preprocessing techniques and 3D image reconstruction for fetal brain MRI. Developed neural network models for fetal brain segmentation and detection.
- Designed a data pipeline to extract 3,856 records, applied data cleaning, transformation via python. Designed a Datawarehouse via MySQL Database. Ensuring data integrity for patient's data.
- Deployed and containerized medical imaging toolchains using docker and docker-compose (MRtrix3, SVRT, NiftyMIC, MONAI), enabling reproducible research environments.
AI Engineer R&D Department - Centre National de la recherche - Lyon, France
(2023-03 - 2023-10)
- Built a artificial neural network for anomaly detection, analytics and ECG cardiac signal classification.
- Monitored and evaluated ML models using performance metrics including F1-score, Precision, Recall, and ROC-AUC, improving predictive accuracy by 35% through iterative hyperparameter tuning and feature engineering.
- Built a Datawarehouse Medallion architecture, transforming ingested data, to support downstream reporting and analytics, reducing processing time by 33%.
