CV du
consultant

BDD

Azure Cassandra Data Databricks HIVE hortonworks snowflake Sqoop

Connaissances fonctionnelles

Business Cloud Coordination Cybersecurity GCP Kafka Leadership Performance pipelines SOFTWARE WEB workflows

Langages

Docker ELK Github Hadoop Pandas PySpark PYTHON scala Spark SQL

Méthodes

AGILE Architecture CI/CD DEVOPS elasticsearch integration Jenkins Management POC SCRUM Testing

Outils

airflow Logstash Polars Teams Unity

Réseaux et Middleware

AWS KUBERNETES Monitoring

Systèmes

APACHE INFRASTRUCTURE

. 2017 - CentraleSupélec / ESSEC: Msc Data Sciences and Business Analytics
. 2016 - Institut Supérieur d’Electronique de Paris (ISEP) : Engineering Degree

RESUME
Senior Data Engineer with over 10 years of consulting experience designing data pipelines and analytical platforms for high-volume environments across various clients. Strong expertise in Python (Polars, PySpark) for high-performance data processing and in deploying data solutions on AWS and Databricks. Currently working as a freelance Tech Lead.

Compétences
• Technical leadership and team coordination as a Tech Lead
• Strong problem-solving skills and performance optimization
• Agile / Scrum project management
• Languages: Python, SQL, Scala
• Technical Stack: Spark, Databricks, Pandas, Polars, Airflow
• Cloud : AWS, GCP
• DevOps : Docker, Kubernetes (EKS), Jenkins

Formations
• 2016 – 2017 CentraleSupélec / ESSEC: Msc Data Sciences and Business Analytics
Courses: Machine Learning, Deep Learning, Optimization, Business Strategy, Big Data Analytics, Massive DataProcessing

• 2013 - 2016
Institut Supérieur d’Electronique de Paris (ISEP) : Engineering Degree
Courses: Web Development, Software Engineering, Algorithms, Cybersecurity

Langues
• English: Professional proficiency (TOEIC: 845/990)
• Spanish: Intermediate


Expériences Professionnelles

Decathlon
SINCE AUGUST 2021
Freelance Tech Lead Data Engineer (Paris / Lille)
• Designed distributed data pipelines using PySpark (Databricks) and optimized lightweight processing with Polars
• Industrialized data workflows orchestrated with Apache Airflow
• Built a data architecture on AWS with data governance via Unity Catalog
• Deployed Polars workloads on Kubernetes to improve performance and scalability
• Implemented automated testing (unit & integration) and data quality checks using Great Expectations
• Designed and implemented a data CI/CD pipeline using GitHub Actions
• Optimized complex data flows (traceability: 50 million records/day)
Stack : Python, PySpark, Pandas, Polars, Databricks, Airflow, AWS, Kubernetes (EKS), CI/CD
Rexel
FEB 2021 - JULY 2021
Data Engineer – Paris

• Supported teams in India in implementing data reference frameworks
• Managed projects using Agile methodology
• Provided consulting on existing IT infrastructure
Stack : Python, Pandas


Lacoste
JUNE 2019 - FEB 2021
Data Engineer – Paris

• Acted as a direct interface with business teams: requirement gathering, prioritization, and translation without a Product Manager
• Built delivery delay classification based on root cause analysis rules
• Developed stock evolution forecasting models
Stack : Python, Snowflake, Databricks, Azure


Boursorama
FEB 2018 - JUNE 2019
Data Engineer – Paris

• Leveraged data using Spark Streaming and Kafka
• Implemented real-time fraud detection
• Developed customer scoring models
• Set up monitoring and automation using Jenkins
• Implemented indexing and search using the ELK stack
• Optimized Spark on YARN projects
Stack : Python, ELK, CASSANDRA, Kafka, Scala, Hadoop


Data2B
SEPT 2017 - JAN 2018
Data Scientist- Paris
• Developed a Proof of Concept (PoC) for a car-sharing company
• Ingested both internal and external data (including open data)
• Implemented multiple mathematical models
• Predicted data evolution trends
Stack : Python, Pandas, Scikit Learn


HSBC
OCT 2014 - SEPT 2016
Engineer Internship – Paris
• Installed and configured a Hortonworks cluster
• Implemented a Big Data platform on an HSBC Hong Kong cluster
• Queried data from multiple sources to profile specific customer segments
• Transferred data using Sqoop
• Collected log data using Webtrends and Logstash
• Scheduled workflows using Oozie
• Implemented indexing and search using Elasticsearch
Stack : Python, ELK, Pig, Hive, Hadoop