À propos de Simon
Data Engineer with 4+ years of experience designing, building, and optimizing production-grade data platforms on Azure and Databricks. Specialized in scalable ETL pipelines, real-time data processing, and analytics solutions using Python, Scala, Spark, Kafka, and SQL. Passionate about delivering reliable, high-performance data solutions that enable business insights and data-driven decision making.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Nice (jusqu’à 50 km), Lyon (jusqu’à 10 km), Paris (jusqu’à 10 km)
Expériences
- Amadeus - Travel IntelligenceData EngineerVOYAGE & TOURISMEjuin 2025 - Aujourd'hui (1 an et 2 mois)Nice, France- Developing a Kafka streaming pipeline that processes around 800 shopping events per second to support real-time travel demand insights.- Developed PySpark data pipelines on Databricks to compute passenger traffic insights (top 10 origin cities/countries) by destination and time grain, processing 635M+ bookings.- Migrated legacy Perl/SQL processing rules to PySpark on Azure, achieving a 55% rules reduction and reducing operational complexity for production runs.- Orchestrated and scheduled Spark pipelines in Azure (Synapse, Airflow), ensuring stable, on-time production executions.- Designed and implemented Jenkins-based CI/CD for PySpark data products on Databricks, streamlining automated, reliable deployments.
- Amadeus - Hotel Business IntelligenceData EngineerVOYAGE & TOURISMEfévrier 2022 - mai 2025 (3 ans et 3 mois)Nice, France- Reduced booking data extraction runtime by 95% by re-engineering a legacy C++ process into Spark/Scala on Databricks.- Developed and maintained production ETL pipelines for market KPIs used by internal stakeholders and external clients, strengthening pipeline reliability and data quality.- Migrated legacy Spark/Scala data pipelines to Microsoft Azure, improving BI report query performance and data access.- Built a backfill/reprocessing workflow to resolve data discrepancies and restore data completeness.- Built a configuration-driven email delivery process for market insights reports, supporting multiple file formats and delivery frequencies.- Deployed and optimized an AI model on Azure to generate booking forecasts used in market insights reports.- Defined Prometheus monitoring rules for pipeline incidents, improving failure detection and reducing delayed report deliv eries.- Documented workflows, processes, and architecture to facilitate knowledge sharing and mentorship of newcomers.
- Capgemini - Innovation DivisionMachine Learning Internmars 2021 - août 2021 (5 mois)Lyon, France- Built and optimized an object detection model, leveraging computer vision algorithms to boost identification accuracy and performance.
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Formations
- Licence InformatiqueUniversité Claude Bernard Lyon 12019
- Master 2 Data ScienceUniversité Claude Bernard Lyon 12021