Freelancer profile translated to English.

Description

Senior Data Engineer, I support large companies (Caisse des Dépôts, Stellantis, RTE) in the design and industrialization of their Data platforms, from ingestion to valorization.

🛠️ Technical Stack:

Azure Cloud: Data Factory, Databricks, Synapse, DevOps, Delta Lake

Big Data: Spark, Hadoop, Kafka, Hive

Languages: Python, Java, SQL

CI/CD: Azure DevOps, Jenkins, Git, Control-M

💼 What I Offer:

Design of scalable ETL/ELT pipelines

Data Lake Architecture (Azure & on-premise)

Migration to Azure Cloud

Spark & Cloud Cost Optimization

Implementation of Data CI/CD

🏆 Latest Projects:

Lead Data Engineer at Caisse des Dépôts (Prioréno project - national energy mapping), previously at Stellantis and RTE.

Languages

French
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

Caisse des dépots
Lead Data Engineer
September 2023 - April 2026 (2 years and 7 months)
Area of intervention: Design and development of Databricks workflows for processing and analyzing large volumes of data. Creation and optimization of Azure Data Factory pipelines for data ingestion, transformation, and orchestration. Improvement of CI/CD processes related to data pipelines via Azure DevOps (version management, deployment automation). REST API calls for collecting energy data (ENEDIS, GRDF). Data modeling and structuring in Azure Data Lake and Delta Lake.
Caisse des Dépôts
Lead Data Engineer
July 2021 - July 2023 (2 years)
Area of intervention:
Implementation of geographical data
Implementation of Kafka Producer/Consumer
Execution of Python unit tests
Automation of Spark processing
Creation of Jenkins jobs for continuous integration
Documentation of scripts (.doc, Confluence)
Management of versioning for developed programs (git)
Development of Python scripts for monitoring processing
Analysis of generated results
Construction automobile
Data Engineer
May 2019 - April 2021 (1 year and 11 months)
Project profile: The project involves characterizing the stresses generated by customers on vehicles. Perform a statistical representation of a scalar distributed across a given population. It provides usage distribution among customers (number of braking actions, number of door openings/closings, etc.). A project profile therefore justifies a design choice and the associated validation needs.
Area of intervention:
Implementation of processing for data ingestion into a Data Lake (reception, transformation, ingestion).
Conversion of decoded data into usable data using Python scripts.
Documentation of scripts.