Freelancer profile translated to English.

Description

I am a Senior Data Engineer with 8+ years of experience, designing and industrializing complex data pipelines on GCP, AWS, and Snowflake. Specializing in dbt and Dagster, I work on critical data stacks for scale-ups and large companies that cannot afford downtime.

💡 What I can bring to you:

✅ Creation and optimization of Data Warehouses (Snowflake, BigQuery, Redshift)
✅ Implementation of robust ETL pipelines (Dagster, Airflow, dbt, Python, Airbyte)
✅ Automation and industrialization of data workflows
✅ Cost and performance optimization on the cloud
✅ Fraud detection and advanced data analysis

🎯 Some recent achievements:

🔹 -30% cost on BigQuery thanks to query and modeling redesign.
🔹 +50% speed in data processing with an optimized pipeline (Dagster + dbt).
🔹 Creation of a complete data stack on GCP at Lucca (CI/CD, BigQuery, dbt, Airbyte) in 1 month.
🔹 Reduction of false positives in fraud detection for an insurer, through algorithm optimization.
🔹 Automation of dashboards providing reliable data for executive committee reporting.

With 6+ years of experience in data engineering, I have supported startups (Qantev, Lucca) and large companies (Contentsquare, Richemont, Dior...) on strategic data modernization and industrialization projects.

👨‍💻 Technical Stack:

🔹 Cloud: GCP, AWS, Azure
🔹 Orchestration: Airflow, Dagster
🔹 Transformation: dbt
🔹 Storage & DWH: BigQuery, Snowflake, Redshift
🔹 Infra: Terraform, Docker, GitHub Actions
🔹 Languages: Python, SQL

📩 Need an expert to optimize your data architecture?

Contact me and let's discuss your project 🚀

Industry field of expertise

Languages

French
Native or bilingual
English
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Neoen
Lead Data Engineer
ENERGY AND UTILITIES
July 2025 - Today (11 months)
Paris, France
Technical lead for the Energy Management team (16 people), ensuring the quality, robustness, and scalability of developments.
Implementation and maintenance of good development practices (tests, CI/CD, automated deployments to staging & production via GitLab).
Administration and optimization of Kubernetes deployments (AKS): pod management, incident resolution, and improvement of application availability 24/7.
Design, development, and orchestration of robust and scalable data pipelines with Dagster in close collaboration with Quantitative Analysts.
Implementation of infrastructure as code with Terraform to manage critical resources (SQL Server, GitLab repositories, Azure Key Vault secrets).
Definition and dissemination of best practices in data management, training, and support for the Energy Management team.
Active contribution to the design of a reliable, testable, and secure architecture, integrating monitoring and alerting mechanisms to ensure data integrity.

💡 Technologies: Azure, Dagster, Python, Terraform, GitLab, CI/CD, Kubernetes, SQL Server
Python SQL Kubernetes Microsoft Azure DevOps
Betclic
Senior Data Engineer
ENTERTAINMENT AND LEISURE
February 2025 - June 2025 (4 months)
Paris, France
Design and deployment of data pipelines related to user retention issues.
Implementation of data ingestion, modeling, and transformation processes within a Snowflake-based Data Lake.
Automation and industrialization of data pipeline deployment using Airflow and CI/CD practices.
Technical and functional writing of documentation associated with the projects completed.

💡 Technologies: AWS, Airflow, dbt, Snowflake, GitHub
Snowflake Amazon Web Services GitHub Actions DBT Airflow
Qantev
Senior Data Engineer
BANKING AND INSURANCE
November 2024 - February 2025 (3 months)
Paris, France
Development and deployment of a backend system to apply insurer-specific exclusion rules, automatically identifying uncovered treatments and reducing manual claim validation efforts.
Design and implementation of ETL pipelines with Dagster to extract, transform, and integrate customer data into the database, thereby improving data reliability and accessibility for analysis teams.
Optimization of a fraud detection model, significantly reducing false positives and increasing detection accuracy, thereby strengthening confidence in the system and minimizing financial risks for insurers.
Refactoring of the codebase, modernizing critical data processing components and improving maintainability, scalability, and execution performance.
Implementation of dbt with duckdb to automatically determine rules for detecting potential fraud.

Technical Stack: Azure, Dagster, Django, Python, Terraform, GitHub
Python Django Rest Framework Dagster