Freelancer profile translated to English.

Description

🚀 Lead Data Engineer | Multi-Cloud Expertise (GCP & Azure)

10 years of Data Experience / 5+ years in Cloud Engineering

Helping companies transform and industrialize their data platforms is at the core of my profession. With a solid background combining classic Data culture (BI/SQL) and native Cloud technologies, I am involved in the design, development, and deployment of scalable pipelines.

🛠Key Skills:

GCP Environment:Advanced mastery of the Google Cloud ecosystem (BigQuery, Dataflow via Apache Beam, Cloud Composer/Airflow, Pub/Sub).
Azure Environment:Expertise in Databricks (PySpark), Azure Data Factory, Synapse, and SQL Database.
Industrialization & DevOps:Complete infrastructure automation viaTerraform(IaC) and CI/CD pipeline implementation.
Migration & Architecture:Expertise in migrating Legacy systems (SSIS/SQL Server type) to modern Cloud architectures (Medallion Architecture).

📈 Notable References:

I have had the pleasure of bringing my expertise to leaders in various sectors:

Luxury (Givenchy/LVMH):Design and Optimization of complex data flows on GCP.
Automotive (Stellantis):Technical design and development on massive Data environments.
Media (TF1) & Finance (Natixis):Design of robust architectures and data lifecycle management.

🤝 Why work with me?

Beyond the technical aspects, my role asLead Engineerallows me to bridge the gap between business needs and technical constraints, while ensuring maintainable, secure, and optimized code (FinOps).

Available for hybrid missions (Remote and On-site) in Paris and the Île-de-France region.

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

GIVENCHY
Lead Data Engineer / GCP Architect – DataPlatform Project
LUXURY GOODS
June 2023 - Today (3 years and 2 months)
Paris, France
Challenge:Migration of a legacy ecosystem (SQL Server/SSIS) to a modern and scalable ELT platform on GCP.

🎯Context & Objectives:
The goal was to replace an aging and costly on-premise infrastructure with a cloud-native architecture capable of supporting the brand's international growth and unifying Retail, E-commerce, and Marketing data.

🛠️ Achievements (The "How")
Architecture & Ingestion (T1):Design and deployment of streaming/batch pipelines via **Dataflow (Python/Apache Beam)**, ensuring the ingestion of millions of daily events with "Late Data" management.
Modeling & Governance (T2):Migration of complex business logic toBigQueryusingDataform (SQLX)to ensure versioning, documentation, and data quality (assertions).
Infrastructure as Code (IaC):Complete infrastructure automation via **Terraform**, enabling 100% reproducible multi-environment deployment (Dev, Staging, Prod).
Orchestration & CI/CD:Implementation of complex workflows underCloud Composer (Airflow)and continuous integration via Cloud Build.

📈 Results & Impact (The Numbers):
OPEX Reduction: -30%on overall cloud billing.
Performance:Data availability time (Time-to-market) reduced by80%(from D+1 to visibility in minutes).
Reliability:Pipeline success rate increased from 85% to99%thanks to automation and data testing.
Google Cloud Platform (GCP) BigQuery Dataflow Python Terraform
GROUPE TF1
Data Engineer Azure Databricks
PRESS AND MEDIA
February 2020 - February 2023 (3 years)
Boulogne-Billancourt, France
Challenge:Industrialization and scalability of audience and advertising data flows via a Lakehouse architecture.

🛠️ Achievements (The "How")
Medallion Architecture:Implementation ofBronze, Silver, and Goldzones onAzure Data Lake Storage (ADLS Gen2)using theDelta Lakeformat to ensure transaction ACID compliance and data quality.
Databricks & PySpark Development:Design of optimized Spark notebooks and jobs to transform massive volumes of audience data in real-time and batch.
Python Industrialization:Creation of modular Python libraries and classes to standardize the ingestion of heterogeneous data into the Data Lake.
Performance Optimization:Advanced management of partitioning and indexing to reduce compute costs and speed up analytical queries.

📈 Results & Impact:
Productivity:Standardization of data pipelines reducing development time for new flows.
Reliability:Implementation of a Lakehouse architecture enabling a "single source of truth" (Gold Layer) for business reports.
Performance:Optimization of Databricks clusters, reducing processing times on large audience volumes.
Microsoft Azure Azure Databricks Azure Data Factory CI/CD Python
NATIXIS
BI Consultant Azure (POC)
BANKING AND INSURANCE
October 2019 - February 2020 (4 months)
Paris, France
Challenge:Design and implementation of a POC (Proof of Concept) to automate sales reporting via a 100% Azure Cloud architecture.

Description:As part of the modernization of steering tools, I implemented an end-to-end decision-making architecture, enabling the transformation of raw data (flat files) into interactive insights for business departments.

Key Achievements:
Co-Administration & Infrastructure:Provisioning of Azure resources (**Azure SQL Database, Storage, Analysis Services**) via the Azure portal.
Ingestion & ETL:Development of integration flows withAzure Data Factory (ADF)to automate the ingestion of source files (CSV) into SQL Server.
Data Warehouse Modeling:Design of the star schema (fact and dimension tables) and creation of optimized SQL views.
Semantic Architecture:Implementation and deployment of the tabular cube underAzure Analysis Services (AAS)to ensure optimal calculation performance on large volumes.
Data Visualization:Design, development, and production deployment of interactive dashboards under **Power BI**.

Results:
Technical validation of the POC, allowing for a transition to larger-scale industrialization.
Centralization of sales data in a secure "Single Source of Truth" on Azure.
Microsoft Azure Azure Data Factory Microsoft Power BI Azure Analysis Services Azure Data Lake Storage