Freelancer profile translated to English.

Description

Data Architect / Senior Data engineer for over 7 years, I have participated in several missions to deploy Data platforms on the cloud using a very modern technical stack to ensure the availability, autoscalability, and reliability of solutions to meet various business needs.

Cloud "native" Data engineer, I have had the opportunity to work on Data solutions on the market's leading cloud providers: AWS and GCP to design, implement, and deploy data storage, computation, and analysis solutions. I have also worked on the industrialization and automation of machine learning algorithms on the cloud.

Languages

French
Native or bilingual
Arabic
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

bpifrance
AWS Tech Lead
BANKING AND INSURANCE
October 2021 - August 2022 (10 months)
Maisons-Alfort, France
Development of a data product within the finance and risk scope for:
- Ingesting and transforming data: PySpark Glue
- Developing APIs to expose data: MongoDB, Flask
- Industrializing and automating the deployment of data pipelines and APIs: Jenkins / Terraform
- Mentoring the development team
- Contributing to the implementation of the Data Mesh architecture

Agile work environment: Safe Framework
Unify Groupe
Lead Cloud Data Engineer
ENTERTAINMENT AND LEISURE
April 2021 - Today (5 years and 2 months)
Paris, France
Within the Data office, responsible for the group-level data lake: over 20 high-traffic sites (Marmiton, Doctissimo, Aufeminin, Lesnumériques...)
ML Ops: Deployment of machine learning algorithms
Optimization of the different layers of the data lake:
- Storage: GCS & BigQuery
- Computation: Apache Beam, Databricks on GCP
- API: migration of APIs to a new Serverless architecture
Implementation of the architecture and development of data integration flows in streaming and batch mode
Implementation of dbt (ELT) for data transformation
Implementation of CI/CD for various data engineering and data science projects.
Dissemination and sharing of development best practices within the data engineering and data science teams.
Backlog definition, sprint planning facilitation, and development tracking
FinOps
Technical stack: Python, Airflow, Kubeflow, GCP, Google Analytics, Java, SQL, GitLab, PySpark, Apache Beam
Python SQL Airflow GCP Kubeflow CI/CD Docker Terraform dbt API
Neoxia
Lead Data Engineer & Manager Data Factory
DIGITAL AND IT
January 2018 - April 2021 (3 years and 3 months)
Paris, France
Several missions with major clients:
Client: Teréga
Project: Implementation of a data exchange platform
Missions:
- Platform architecture on AWS (service selection)
- Application layer development (Python)
- Infrastructure as Code (Terraform)
- CI/CD implementation
Tools & technologies: Python, Terraform, AWS (API Gateway, S3, Lambda, DynamoDB, SNS, SQS)

Client: Teréga
Project: Implementation of a cloud reporting data warehouse (GCP)
Missions:
- Service selection: storage (GCS), data warehousing (BigQuery), monitoring (Data Studio &
Datadog), and data ingestion (Cloud Composer)
- Automation and industrialization of data warehouse infrastructure creation and
CI/CD pipeline development
- Tech lead: training and support of the development team (data
engineers), code review, implementation of best practices (code, versioning, queries...)
Tools & technologies: Python, Terraform, GCP (GCS, BigQuery, Cloud Composer, Cloud Function, Cloud Scheduler...), Monitoring (Data dog & Stackdriver), Datastudio...

Client: Éditions Lefebvre Sarrut (ELS)
Project: CRM API implementation
Missions:
- Implementation of the database architecture to centralize data from
subsidiaries' clients.
- Implementation of the data integration solution
- Solution automation and industrialization (CI/CD, IaC)
- Cross-functional: Monitoring of API implementation

Tools & technologies: Python (ETL), Terraform, AWS (AWS Batch, VPC, S3, Lambda, EC2), Jenkins, Docker

Client: Pernod-Ricard
Project: Migration of data tools to Azure
Missions:
- Definition of the new architecture for data platform tools on Azure
- Migration of data pipelines from Apache Airflow to Azure Databricks and Azure Data
Factory.
Tools & technologies: Python, Azure (Azure Databricks, Azure DevOps, Azure Datalake Storage, Azure Data Factory)

Client: Pernod-Ricard
Project: Data Platform Implementation
Missions:
- Contribution to the implementation of platform tools for data storage, processing, and
exploitation on the cloud (Provider: AWS)
- Data pipeline architecture and development
- Code review (Python, Airflow)
- Support for data scientists / data engineers
Tools & technologies: Python, Apache Airflow, Apache Superset, Power BI, Docker, AWS (ECS, Athena, ECR, Cloud9, S3, ..)
Amazon Web Services Microsoft Azure Google Cloud Python SQL Docker PostgreSQL ETL Airflow Databricks CI/CD Jenkins Pyspark Terraform Snowflake