Freelancer profile translated to English.

Description

Who am I?

Passionate about theDatauniverse, I combine a stronglearningandadaptation capacitywith clear technical communication.Autonomousand **rigorous**, I explore solution architectures to understand their mechanisms and propose concrete improvements.

With an analytical andproblem-solvingoriented mind, I invest myself in each project with confidence and transparency, seeking to perfect my skills and broaden my expertise.

Committed and creative, I know how to analyze complex or obsolete systems, adapt to them, and design innovative and relevant solutions. My career has led me to cross-functional roles, at the intersection ofarchitecture designandtechnical implementation(Data, ETL, MLOps, Cloud), with the goal of making environments more fluid, industrialized, and standardized.

Areas of Expertise

Architecture and industrialization of Big Data / Cloud / MLOps platforms
Optimization of ETL / Spark / Python workflows
Design and governance of Data Lake / Data Warehouse
Orchestration and automation of pipelines (Airflow, Azure Data Factory)
Supervision, monitoring, and performance of data platforms
Multi-source integration and data quality control
Migration On-Prem → Cloud and technological transition (Kubernetes / Openshift / Terraform)
Reverse engineering & legacy architecture (audit, documentation, and modernization of existing systems)
Technical documentation (DAT) of platform and system architectures

Languages

French
Native or bilingual
Arabic
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

Groupama
Data Technical Architect (AI)
BANKING AND INSURANCE
January 2024 - September 2025 (1 year and 8 months)
Paris, France
Context
Within the AI Factory, supporting teams in the redesign of the Machine Learning industrialization platform and the transition to a standardized MLOps platform, compliant with architecture and security standards. Design of a scalable, secure, and maintainable AI ecosystem, capable of supporting new use cases (ML and LLM).
Actions / Projects
ML platform analysis & standardization: study of the existing system (OpenShift on-premise) and preparation for standardization, in compliance with architecture and security standards.
Python workflow optimization: improvement of DataFrame performance and memory management to enhance the scalability of ML workloads.
Design of new AI platform (Azure): definition of a unified platform adapted to ML/LLM workloads, aiming for MLOps maturity level 2 and compliant with group standards.
Documentation & support: writing architecture documents (DAT), technical capitalization on Confluence, and support for Data, ML Engineering, and Ops teams.
Results
Rapid skill development on Azure (portal, Data Factory, Terraform), with direct contribution to the implementation of the cloud foundation and the industrialization of pipelines.
Technical Environment
Openshift 4 (Kubernetes), OpCon, Oracle RAC, Python 3x, Terraform, Cloud Azure, Azure DataFactory
Openshift 4 (Kubernetes) Azure DataFactory Python 3x Oracle Terraform
Groupama
Data Technical Architect (BigData)
BANKING AND INSURANCE
December 2021 - December 2023 (2 years)
Paris, France
Context
Participation in the redesign of the Big Data platform (Hadoop/BDA) and support for the transition to an event-driven architecture (Kafka, Spark), to enhance modularity, scalability, and data governance. Writing of the Technical Architecture Document (DAT) and optimization of existing patterns to improve processing and support team upskilling.

Actions/Projects
Spark/Mesos optimization: Scala code audit, performance analysis, exploitation of native features, parallelism optimization, and migration to cluster mode to eliminate SPOF.
Mesos → Kubernetes (OpenShift) migration: feasibility study, technical pilot, preparation of Spark Docker images, deployment of the Spark Operator, configuration of NFS/SSD volumes, setup of the Spark History Server, and UI exposure via OpenShift Routes, with impact analysis and macro cost/schedule evaluation.
Documentation & support: writing of the DAT, technical capitalization on Confluence (schemas, diagrams), knowledge transfer, and support for Data Engineering & Ops teams in upskilling.
Results
Rapid adaptation to the Spark/Mesos and MongoDB environment, with optimization of processing and taking on the role of technical referent/architect to support the team and enhance platform performance.

Technical Environment
Linux Redhat, Spark 3.2 (structured streaming), Kafka Confluent, MongoDB 4.2, Mesos 1.9, Oracle DB, Ansible, OpCon, Oracle BDA Bigdata Appliance (Cloudera), Openshift 4 (Kubernetes).
OpenShift Spark Mesos MongoDB Apache Kafka
Leboncoin
BI Data Engineer
E-COMMERCE
March 2021 - December 2021 (9 months)
Paris, France
Context
Evolutionary maintenance mission for a Dockerized platform on AWS (S3, Athena, Redshift, RDS, EC2). Implementation of BI pipelines from data integration to visualization, orchestrated with Airflow and developed in Python, SQL, and Java (Spark). Automated deployments via Ansible and provisioning with Terraform.
Actions
User support and handling of prioritized ad hoc requests.
Gathering and analysis of business needs in collaboration with Product teams.
Design and development of complex ETL pipelines for optimal data flow.
SQL modeling in a Data Warehouse environment and creation of business KPIs through advanced queries.
Orchestration and automation of processing via Airflow (DAG).
Integration of data from various sources, including Kafka and internal databases.
Archiving Kafka topics to S3 to expand the analysis scope and improve data availability.
Results
Rapid adaptation to the work context and a technical environment based on Shell scripting and Python, which allowed me to develop strong autonomy and make an effective contribution to projects.

Technical Environment
Ubuntu 20.04.2 LTS, Postgresql, AWS (S3, Athena, Redshift), SQL window function, Airflow, Python, Spark (Java), Git, Github, Docker, Kubernetes, JupytherHub, Ansible, Terraform, Travis CI/CD
Ansible Terraform Python Memory optimisation Apache Airflow SQL Analytics