Description

Data Engineer with 6+ years building scalable data platforms on Azure, GCP, and on-premises. Expert in ETL/ELT pipelines (Spark, Airflow, Databricks), SQL/NoSQL optimization, and CI/CD-driven DataOps. Proven ability to architect full data infrastructures from scratch and deliver end-to-end solutions across retail, smart cities, and autonomous vehicle industries. Complementary background in Machine Learning and Deep Learning bridges the gap between raw data and real business impact.

Industry field of expertise

Languages

French
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

Worklife (Groupe Crédit Agricole)
Lead Data Engineer
BANKING AND INSURANCE
March 2026 - Today (4 months)
Paris, France
Took ownership of the entire data stack, leading data engineering initiatives end-to-end
across architecture, orchestration, transformation, deployment, and analytics delivery.
Led the full migration of the Data Warehouse from PostgreSQL to ClickHouse, driving the
project independently from technical assessment and design to implementation and
production rollout.
Evaluated costs, trade-offs, and performance implications of the new architecture, and
delivered a production-ready platform that reduced dashboard loading time from 5 minutes
to 5 seconds
Re-engineered several critical data workflows to improve reliability and maintainability,
introducing unit and integration tests across transformation pipelines.
Built and industrialized data models with dbt, orchestrated pipelines with Airflow, and
supported deployment processes in Kubernetes-based environments.
Revived the usage reporting project, which had been blocked for 3 years due to reliability
issues, by defining and executing a robust delivery strategy leveraging ClickHouse
capabilities.
SQL Apache Airflow Python (Programming Language) Clickhouse Kubernetes
ARISTID Retail Technology
Data Engineer
June 2024 - Today (2 years and 1 month)
Paris, France
- Designed and built multi-tenant REST APIs (FastAPI/Python) handling bulk data ingestion and search across MongoDB and PostgreSQL for major retail clients (Auchan, Carrefour, Maxeda, BUT)
- Architected a data pipeline platform from end-to-end with Apache Airflow orchestrating 10+ ETL workflows processing diverse data sources (SFTP, APIs, CloudFlare, SAP)
- Implemented event-driven architecture using RabbitMQ for real-time asynchronous data processing across multiple client environments
- Integrated Apache Spark for large-scale data processing, optimizing execution times for high-volume SDO counter computations
- Built a multi-client CI/CD system on GitLab enabling on-demand per-client deployments with automated credential generation and infrastructure provisioning
- Developed a monitoring platform for Airflow, APIs, and RabbitMQ with Microsoft Teams alerting
- Managed database infrastructure: PgBouncer connection pooling, PostgreSQL stored procedures, MongoDB aggregation framework and transactions optimization
- Containerized all services with Docker and managed environments from development to production
- Built and maintained an internal Python library (arislib) providing unified connectors for MongoDB, PostgreSQL, S3, Elasticsearch, and SFTP
- Led international client integrations: Maxeda (Netherlands - Syndigo/SAP), Rona (Canada), and major French retailers
Python Spark AWS S3 Gitlab CI/CD ETL
NHOOD
Data Engineer
January 2023 - June 2024 (1 year and 5 months)
Lille, France
- Establishment of interface contracts to meet various business requirements;
- Development of data flows on Azure Data Factory and Azure Databricks to migrate data from +10 subsidiaries worldwide (Luxembourg, Spain, Portugal, etc.).
- Participation in the implementation of the Agile methodology;
- Initiation of a git repository on AzureDevops and establishment of best practices for branch and code management;
- Added CI/CD pipelines in Azure Devops to the project to test, build, and lint each pull request,
- Data retrieval from multiple sources: Oracle DB, MySql, Web Scraping, etc.
- Data storage on Azure Data Lake Gen 2 ;
- Massive data processing using Pyspark and SparkSql on Azure Databricks;
- Stream scheduling using VTOM ;
- Code Review ;
- Handling of production incidents
Azure DevOps Azure Data Factory Databricks VTOM Python

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Master of Science
Université de Paris
2022
Master, Data science
Master 2 (M2), Computer vision
Université Pierre et Marie Curie
2021
Master 2 (M2), Computer vision

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Data Engineer

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