Freelancer profile translated to English.

Description

Looking for a GCP Data Engineer capable of making your data pipelines reliable, industrializing your processes, and accelerating your data projects? I specialize in data migration, ETL/ELT, BigQuery optimization, and cloud orchestration, with a strong focus on performance, data quality, and production deployment.

I assist clients in designing robust pipelines, developing processing in Python, Spark, or Polars, automating deployments, and monitoring flows in complex GCP environments. My experience includes high-volume contexts, multi-source architectures, and projects where reliability, maintainability, and speed are essential.

I regularly work on deliverables such as ETL/ELT pipelines, BigQuery processing, migrations to GCP, quality controls, CI/CD industrialization, and technical documentation. I bring a dual value: solid technical expertise in the cloud data ecosystem and a pragmatic, results-oriented approach.

Here is also a very short version for the hook field:

GCP Data Engineer specializing in data migration, ETL/ELT, BigQuery, Python, Spark, and Polars. I help companies design reliable, high-performance, and industrialized pipelines on GCP.

Industry field of expertise

Languages

French
Native or bilingual
English
Native or bilingual
Fula
Native or bilingual
Spanish
Conversational

Workplace preferences

Can work on-site

Paris (up to 50km), Puteaux (up to 10km)

Suez
Senior Data Engineer
March 2026 - Today (3 months)
Puteaux, France
Context: Multi-target data migration project for Suez.

- Design and industrialization of data processing in a cloud environment.
- Development of extraction, transformation, and loading pipelines in Python / Polars.
- Orchestration of flows via Cloud Workflows and deployment of processing on Cloud Run.
- Management of file exchanges and storage on Google Cloud Storage.
- Implementation of transformation rules, quality control, and data validation.
- Monitoring of processing, anomaly correction, and continuous improvement of robustness and maintainability.
- Technical supervision and contribution to the choice of project tools and environments.

Stack:GCP, GCS, Cloud Run, Cloud Workflows, Pub/Sub, Salesforce, IBM Maximo, SQL, Python, PySpark, Polars, Pandas, Flask, Azure DevOps, Git, Docker, YAML, Dataiku.
Google Cloud Platform Spark SQL Big Query ETL/ELT
Apside
Cloud Data Engineer
September 2023 - January 2026 (2 years and 4 months)
Boulogne-Billancourt, France
Missions:

Servier
• Extraction of Oracle data with Spark then loading to GCS or BigQuery.
• Migration of large files to Cloud Storage with Rclone.
• Orchestration of migration workflows via Argo CD.
• Automation of GCP services with Terraform and downstream transformations with dbt and SQL.
• Validation of data models and technical documentation of pipelines

MGEN
• Support for modernizing data infrastructure on GCP and optimizing flows.
• Cost and performance optimization on Google Cloud Platform.
• Data management and transformation with BigQuery, Spark, and Python.
• Extraction of HBase data to GCP in a NoSQL to SQL context.
• Loading data with Spark to BigQuery and considering business needs for table creation.
• Writing technical documentation and implementing unit tests for data types

Internal projects
• Development of Python scripts to extract key information from CVs.
• Classification and extraction tasks around LLMs, with Docker containerization

Stack: GCP, Spark, Python, HBase, SQL, BigQuery, Dataproc, Dataflow, Airflow, GitLab CI/CD, dbt, Terraform, Rclone, Argo CD, Docker, Pandas, BeautifulSoup, Scrapy, OpenAI, Ollama.Docker, Confluence.
Renault Group
Cloud Data Engineer
August 2020 - September 2023 (3 years and 1 month)
Context: migration of historical data from an HDP cluster to a GCP architecture.
• Participation in the migration of large volumes of data to Google Cloud Platform.
• Ingestion of CSV / JSON files and data transformation with Python, Java, Scala, and Spark.
• Data quality analysis and SQL testing on BigQuery.
• Use of Dataproc, Dataflow, and Airflow for execution and automation of processing.
• Implementation of CI/CD pipelines with GitLab and deployment of processing in different environments.
• Work on very large tables, with optimization of queries and processing.