Freelancer profile translated to English.

Description

Passionate about data projects, I have worked during my 5 years of experience on various projects with different technologies and sectors. As projects progressed, I gained a good understanding of development best practices, especially with the Python language, which allows working on web development or data projects, which constitutes my strength today.

Industry field of expertise

Languages

Arabic
Native or bilingual
French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km), Lyon (up to 20km), Lille (up to 20km)

Safran Nacelles
Cloud Data Engineer
AVIATION AND AEROSPACE
June 2024 - Today (2 years and 2 months)
Le Havre, France
**Stakeholders**: Various departments at SNA

• Talend Developments:
• Ingesting SAP data in RAW mode to AWS S3
• Bulk ingestion of small and medium-sized tables using the icontrol module
• Ingestion of very large tables in batch
• Data Engineering in AWS
• Creation of a Python package to catalog SAP tables in a Glue database in iceberg format, integrating partitioning, updating or overwriting tables as needed.
• Transformation of Raw data to Curated and Product data according to business needs, cataloged in Glue, for querying in Athena or reporting on Power BI
• Creation of a rapid industrialization system for SQL queries developed by the business to give more autonomy to business users for exploiting RAW data using Lambda
• Orchestration of different data flows using Step Functions, based on an Event Driven architecture using a DynamoDB database and EventBridge.
• Automac Project:
• Production chain digitization project requiring reporting refresh every 15 minutes
• Development of various technical components: SQL Queries, Lambdas, Orchestration.
• Industrialization via Terraform.
Technical environment: Python (3.9 & 3.12), Spark, Iceberg, SQL, AWS (S3, Glue, Lambda, Athena, EventBridge, StepFunction,
DynamoDB), Gitlab, Gitlab CI, Terraform.
Generali France
Data Engineer
BANKING AND INSURANCE
October 2023 - Today (2 years and 9 months)
Paris, France
Stakeholders: Client Value Team – CVM (Client Value Management) Project
Achievements:
• Python Development:
• Following the acquisition of "La Medicale" insurance by Generali, the need was to integrate Generali customer values into the CVM (Client Value Management) application
• Real-time CVM enrichment
• Development of new features to receive Kafka streams from "La Medicale" and process the data before sending it back
• Development of unit tests
• Integration of "La Medicale" data into MongoDB
• Development of a Web App with Streamlit for functional monitoring of CVM data:
• Search interface to find all references for an ID in the databases
Snowflake (After Batch processing) MongoDB (After real-time processing)
• For an ID, a report on the value of the contract or customer
• A timeline to display all references chronologically to track customer or contract value movements
• Data Engineering:
• Development of data processing batches with PySpark to integrate "La Medicale" data into Generali's Hadoop databases
• Evolution of existing batches as requested by the client team
• Development of DAGs on Airflow to enable batch execution
Technical environment: Python (3.7 & 3.10), Spark, Hadoop, SQL, Airflow, MongoDB, Streamlit, Snowflake, Snowpark, Jupyter,
Docker, Kubernetes, Rancher, Cloudera
Python Spark Snowflake Hadoop MongoDB Docker Kubernetes Streamlit Airflow
Cleyrop
Python Developer & Data Engineer
TECH
January 2023 - September 2023 (9 months)
Paris, France
Stakeholders: CPO, PM, Client Project Managers
Achievements:
• Product:
• Development of an internal Python library that allows:
• Reading datasets in Iceberg format
• Making datasets available as PySpark Pandas objects
• Listing datasets in a project
• Abstraction of these different operations for the user to optimize UX
• Development of unit tests for the mentioned functionalities
• CI/CD with Gitlab CI and semantic release
• Creation of a Docker image for Jupyterlab that includes the developed library and necessary services for the data layer, such as Spark, Iceberg, and Nessie
• Service (Client Atout France):
• Bug fixing in existing pipelines developed with Python
• Optimization, improvement, and documentation of existing systems
• Refactoring of various functionalities using the PySpark Pandas API
Technical environment: Python, Spark, Iceberg, Jupyter Notebook, Git, Gitlab CI/CD, Docker, Kubernetes
Python Docker PySpark Spark Gitlab Gitlab CI/CD Kubernetes