Description

La plupart des plateformes data ne posent pas problème à cause de la technologie.

Elles posent problème à cause du design, du manque d’ownership, et de coûts mal maîtrisés.

J’aide les entreprises à construire et faire évoluer leur plateforme data, tout en optimisant les coûts Snowflake et BigQuery.

Avec plus de 8 ans d’expérience, j’ai travaillé sur des environnements data complexes (banque, pharma, media), avec des volumes importants et des contraintes métier fortes.

J’interviens de bout en bout, de la conception à la mise en production.

Ce que j’apporte :

· Pipelines data end-to-end (batch & streaming)

· Modern data stack (Snowflake, BigQuery, dbt, Airflow, Spark)

· Architecture plateforme (cloud, Kubernetes, CI/CD, Terraform)

· Optimisation des coûts data (approche FinOps)

Impact typique :

· Traitement de volumes de données à l’échelle TB

· Amélioration des performances et de la fiabilité

· Réduction des coûts Snowflake / BigQuery (20–50%)

Je construis des plateformes data qui scalent techniquement et financièrement.

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

GROUPE CCF
Senoir Data engineer
January 2023 - Today (3 years and 5 months)
Designed & Implemented:
Designed and implemented robust data pipelines for end-to-end data collection, ingestion, transformation, and storage using dbt for modeling and Snowflake for warehousing.
Platform Engineering: Architected and maintained the foundational infrastructure using container orchestration (Kubernetes) and infrastructure-as-code (Terraform), ensuring scalability, resilience, and seamless integration across diverse data pipelines.
Collaboration:
Partnered with data scientists, developers, and stakeholders to align technical solutions with business requirements.
Automation:
Built and maintained CI/CD workflows using Git, Jenkins, Maven, Docker, and Kubernetes significantly reducing deployment time.
Optimization:
Automated routine tasks with bash scripts and Terraform, reducing manual intervention.

Technical Environment:
AWS (S3, RDS, Lambda, Glue, Athena), Starburst, Airflow, DBT, Kubernetes, MSK, SQS, Python, Scala, GitLab CI, Terraform, Git

AWS Terraform Airflow starburst DBT
Servier Monde
Senior Data Engineer GCP
PHARMACEUTICALS INDUSTRY
July 2021 - December 2023 (2 years and 5 months)
Implementation of a datalake in R&D on data experimentation of laboratories for Servier biologists, chemists, pharmacologists.

REALIZATIONS
- Complexity: a large volume of data (tens of TB) from screening machines and very complex business rules
- Implementation of batch data ingestion workflow
- Implementation of workflows for data cleansing and exposure

TECHNICAL ENVIRONMENT
Bigquery, Cloud Storage, DataFlow, Python, Argo, Kubernetes, Pyspark, Git, Terraform
Google Cloud Platform (GCP) Python SQL CI/CD BigQuery
Solocal
Senior Data Engineer GCP
ENTERTAINMENT AND LEISURE
November 2020 - May 2022 (1 year and 7 months)
Paris, France
Implementation of a data platform that integrates sales and vendor data and makes it usable by the various Finance and Compensation squads

REALIZATIONS
- Complexity: Many data sources to integrate and various
data types (batch, streaming)
- Implementation of batch data ingestion workflow
- Implementation of streaming data ingestion workflow
- Implementation of workflows for data cleansing and exposure in the Data Platform

TECHNICAL ENVIRONMENT
Bigquery, Cloud Storage, Composer, Airflow, DataForm, Python, Java, Cloud Function, Pub/Sub, Git
Big Query Python SQL Google cloud Gitlab CI Linux scrum