Description

Curieux et passionné, j’aime explorer de nouveaux domaines, ce qui m’a conduit de la robotique à la Data Science, puis finalement à la Data Engineering.

Fort d’une expérience dans des secteurs variés, je me spécialise dans la conception de solutions évolutives, efficaces et innovantes, en tirant parti de la puissance de GCP et de dbt pour relever des défis métiers complexes. Actuellement, je suis à la recherche d’une opportunité en tant que Tech Lead ou Senior Data Engineer/Analytics, notamment sur la stack Airflow/GCP/dbt. En apprentissage continu, la montée en compétences sur de nouvelles technologies pour répondre aux besoins spécifiques d’un client est également une source de motivation pour moi.

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

Sfeir
Lead data engineer
August 2023 - November 2024 (1 year and 3 months)
Paris, France
Moët Hennessy Financial team
- Leading and upskilling a team of 4 data engineers on finance and HR-related projects
- Technical referent and data solution architect on the Airflow, BigQuery, and dbt stack
- Deploying, optimizing, and maintaining large-scale data pipelines, with testing and monitoring (Elementary)
- Contributing to the evolution of Moët Hennessy's data platform
- Aligning data solutions with business needs, particularly for financial analysis (Profit & Loss, Stock valuation) and HR insights.
- Creating a structured training program to upskill individuals on data topics and make them operational
Sfeir
Data Engineer
October 2021 - July 2023 (1 year and 9 months)
Paris, France
Solocal Client insight team
- Optimizing and maintaining data pipelines using the Airflow, BigQuery, and DBT stack.
- Usage of elementary for testing and monitoring.
- Implementing business rules to enhance client insights, including turnover, seniority, and other key metrics, driving actionable data outcomes.
StarCLay
Data Scientist
June 2018 - October 2021 (3 years and 4 months)
Île-de-France, France
LexisNexis
• Find the paragraphs of appeal court decisions (unstructured text) where the judge made a decision Used model: Bi-LSTM + CRF (in Pytorch)
• Determine the outcome of case law decisions from courts of appeal: Used model: ULMFit from fast.ai
• Anonymization of legal decisions: Find surnames and adresses Used model: Spacy NER June 2018 - October 2018: Ponant (Cruise company based in Marseille)
• Revenue Management automatization: Prediction model for daily discount of cruise trips using temporal data. (Experiments with different machines learning models such as LightGBM and RNN)
• Training Ponant's employees on machine learning (how to do machine learning, usage of algorithms).