About Magloire
🚀 Senior Data Engineer | Building Scalable Data Platforms
I design and scale data pipelines that handle billions of rows — growing one system from zero to 300B rows in just 2 years.
My expertise lies in turning raw data into reliable, production-ready systems using:
🐍 Python for flexible, high-performance development
☁️ GCP for cloud-native scalability
⚡ Kafka for real-time event streaming
📊 DBT for robust data transformations
🛠️ Terraform for infrastructure as code
📦 Kubernetes for container orchestration at scale
I specialise in both OLAP (analytics) and OLTP (transactions) systems, ensuring that data platforms are optimised for real-time operations as well as large-scale analysis.
I’m passionate about end-to-end data engineering: from ingestion, orchestration, and transformation to serving data for analytics, ML, and AI applications.
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 10km), Bordeaux (up to 10km), Rennes (up to 10km), Nantes (up to 10km), Lyon (up to 10km)
Experience
- H&MData EngineerFASHION AND COSMETICSJuly 2022 - Today (3 years and 11 months)Stockholm, Sweden-Build large scale data and analytics solutions on GCP-Use modern data/analytics technologies on-premise and Cloud-Efficiently use the GCP platform to integrate large datasets from multiple data sources, analyse data, data modelling, data exploitation/visualisation-Design and Build automated data pipelines-Engineer Data engineering solution on GCP using Cloud BigQuery, Cloud DataProc, Cloud Dataflow, Pub-Sub, Cloud BigTable, Cloud storage Spark, Hive and AI/Ml solutions-DevOps, CI/CD implementation (Terraform)-Extract, load, transform, clean and validate data (Streaming Pipelines)
- ADEO Services
On Malt
Data Engineer Big QueryLOGISTICS AND SUPPLY CHAINJuly 2021 - February 2022 (8 months)Lille, France- Building a supplychain data model (datamart)- Setting up a pipeline flow for a Big Query model,- Definition of the physical model based on a MCD- Extraction/Transformation and loading of petabytes of data via GCP- Creation and monitoring of Batch/Stream pipelines- Dashboard setting up- Creation of PowerBI/Data Studio KPI dashboards- KPI creation- Monitoring of logs/costs - We Dress FairData ScientistE-COMMERCEMay 2021 - May 2021Lyon, FranceCollaborative System filtering Algorithm with NLP Smart search engine :Fetch data from PostgresSQL database (psycopg2)EDAData cleaningData preprocessing ( word 2 vec)Feature engineeringBuild a smart search engine with NLP ( gensim, scipy,nltk)API creation (Fast API - uvicorn)Front-end build with flaskDockerizationCI/CDDeployment with GCP
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Education
- Master DatascienceLe Wagon2021-Using advanced computerized models to extract the data needed ( Python -SQL - Scrapping-API) - Cleaning dataset by removing corrupted data - Exploratory data analysis : 1. Initial analysis to assess the quality of the data 2. Performing further analysis to determine the meaning of the data 3. Performing final analysis to provide additional data screening 4. Statistical inferences 5. Communication - Feature engineering - Applying machine learning algorithms and statistical techniques to resolve business issues. - Applying deep learning algorithms - Developping pipelines and tools to monitor and analyse model performance and data accuracy - API creation (Fast API) - Integrate data from 3rd party services via ETL tools and custom pipelines (GCP) - Deployment to production ( Heroku - Streamlit -GCP ) - CI / CD
Certifications
- DeepLearning.AI TensorFlow DeveloperDeepLearning.AI2020
- Data Engineering, Big Data, and Machine Learning on GCPGoogle2022