Freelancer profile translated to English.

Description

I help companies centralize, make reliable, and industrialize their data on Google Cloud to produce datasets ready for reporting and decision-making. I design robust ELT/ETL pipelines (batch and event-driven), from ingestion to the data warehouse, with a DataOps / CI-CD / IaC approach.

How I can help you

Design and industrialize data pipelines on GCP (multi-source ingestion: API/HTTPS, SFTP, Parquet/CSV files…)
Implement a scalable event-driven architecture (e.g., GCS → Pub/Sub → Workflows → Cloud Run → BigQuery)
Structure and optimize the BigQuery DWH: modeling, partitioning/clustering, costs & performance
Transform & test data with dbt (tests, quality, traceability)
Automate infrastructure and deployments via Terraform + Docker + Cloud Build (IaC + CI/CD)
Secure and make reliable: IAM, Secret Manager, logs/monitoring, incident management

What sets me apart

Strong industrialization culture (quality, monitoring, logs, error management)
End-to-end GCP expertise + orchestration (Airflow/Composer, Workflows)
Ability to frame business needs and deliver quickly (Agile, estimation, backlog)

Typical deliverables

ELT/ETL pipelines + Airflow DAGs, Spark/PySpark jobs
BigQuery models + dbt (sources/staging/marts) + test suites
Infrastructure as Code Terraform + CI/CD pipelines
Documentation, runbooks, monitoring/quality dashboards (as applicable)

Languages

French
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km), Nice (up to 50km), Toulouse (up to 50km), Bordeaux (up to 50km), Lyon (up to 50km)

EDF
Senior Data Engineer
ENERGY AND UTILITIES
November 2022 - February 2026 (3 years and 3 months)
Île-de-France, France
Context: Implementation of a data system aimed at improving the performance of inspection and supervision teams. Field data was centralized and structured to ensure reliable activity tracking, optimize intervention planning, and enhance operational safety and quality. The main challenge is to provide supervisors with a consolidated and reliable view to effectively manage teams and prioritize actions.

Define data retrieval methods by data source.
Identify GCP services for different processing phases.
Develop Airflow ingestion DAGs (HTTPS, SFTP).
Add the reverse-proxy component for connection between databases and GCP.
Implement an event-driven architecture (GCS -> Pub/Sub -> Cloud Workflows -> Cloud Run -> BigQuery) and automate processing with dbt.
Industrialize infrastructure and deployments via Terraform, Docker, and Cloud Build (IaC + CI/CD).
Model data in BigQuery with dbt and secure access (IAM, Secret Manager), accompanied by end-to-end tests and log monitoring.
Industrialize Spark jobs (packaging, parameters, logs, error management).
Contribute to data governance: repositories, master data definitions by domain, alignment with group data strategy.
Implement an ML pipeline on Vertex AI: data quality checks, scheduled batch prediction, and performance monitoring (metrics, drift, errors) via Vertex AI Monitoring.
Technological watch on tools and continuous self-training on AI tools.
Technologies: Python, SQL, Terraform, Terragrunt, Spark (Dataproc), dbt, Docker, Cloud Storage (Delta Lake), Dataplex, Google Cloud BigQuery, Google Cloud Build, Google Cloud Logging, Google Cloud Platform (GCP), Google Cloud Run, Google Identity and Access Management (IAM), Google Pub/Sub, Google Workflows, Vertex AI.
Google cloud Terraform Spark Python SQL
Thales
Data Engineer
TRANSPORTATION
September 2021 - October 2022 (1 year and 1 month)
Île-de-France, France
Context: Implementation of an end-to-end data use case to centralize multi-country transport activity data and adapt it to a retail billing context. The objective is to make data reliable and historical to produce datasets ready for reporting and decision-making. Production of business indicators (activity by zone/period, average amounts, payment methods, revenue by country, average basket, top products) with recurring executions.

Analyze needs with business teams and write technical specifications.
Develop Python scripts for automatic ingestion of Parquet/CSV files to GCS, with logs.
Set up and manage buckets and files in GCS.
Develop SQL in BigQuery and manage access rights (IAM & Admin).
Create, configure, and deploy tables, views, stored procedures, and pipelines in the GCP cloud.
Design data pipelines for collection and extraction from various cloud storage sources, and create transformation models under dbt.
Develop PySpark jobs for heavy transformations (normalization, deduplication, aggregation, enrichment), writing to BigQuery (partitioned/clustered tables).
Manage and analyze incidents in Dev and Prod environments (erroneous data, missing data, data updates).
Design a GCP ELT architecture: Cloud Storage (raw), BigQuery (raw / transform / views), Cloud Composer (Airflow).
Orchestrate the pipeline via a scheduled Airflow/Cloud Composer DAG.
**Technologies**: Apache Airflow, Google Cloud CLI (gcloud), Google Cloud BigQuery, Google Cloud Composer, GCP, Google Cloud Shell, IAM, Terraform, Python, SQL, GCS, ELT, Spark.
Python SQL Terraform Google Cloud Platform (GCP) Spark
Auchan
Data Engineer | Permanent Contract
E-COMMERCE
September 2020 - August 2021 (11 months)
Lille, France
Context: Redesign and migration of data pipelines feeding over 30 Digital Marketing Dashboards used by over 2000 users worldwide from DOMO to GCP and Power BI.

Developed an API for an internal Big Data solution with JAVA (Spring webflux).
Developed and optimized SQL queries.
Performed unit tests with JUnit.
Managed versioning with GIT.
Developed the presentation layer in Angular7, HTML, CSS.
Set up GCP infrastructure and configured CI/CD pipelines on GitHub.
Collaborated with the Dataviz team to create optimized and relevant Dashboards.
Developed ETLs to extract data from various sources (APIs, etc.).
Performed data transformations.
Actively contributed to improving the architecture to optimize performance.
Created user stories, managed the backlog and estimations, enabling efficient project planning.

**Technologies**: Python, Java, CSS, HTML, Angular, Google BigQuery, Google Workflows, Google Cloud Run, Docker, Terraform, Git, CI/CD.
Python SQL Google Cloud Platform (GCP) Docker Java