Freelancer profile translated to English.
Back to original languageAbout Rabeh
Data Engineer | Expert Databricks, Spark, Azure, AWS | 9 years of experience
đ I am a Senior Data Engineer specializing in Azure & AWS
With 9 years of experience in developing Big Data and Cloud projects, I work with large companies to design, build, and optimize their data platforms.
đ Client references:
TF1
TotalEnergies
AXA Direct Assurance
Société Générale
â
What I bring to you:
đ· Cloud & Data Expertise
- Design & selection of data architectures
- Implementation of data lakes, data warehouses, lakehouses
- Large-scale data integration & processing
đ· Big Data Development & Administration
- Spark (Scala / Python/ SQL)
- Databricks (admin, dev, CICD with Databricks bundles)
đ· Cloud Platforms:
- Azure: Data Factory, Databricks, Synapse, Fabric
- AWS: Glue, S3, EMR, EC2, Lambda
đ· Implementation of CI/CD & Infrastructure as Code
- Azure DevOps
- Terraform
- Databricks bundles
đĄ What I prioritize:
- An agile approach
- Controlled planning
- Respect for deadlines
đ© Let's discuss your project!
I am available to chat directly here on Malt.
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km), Nice (up to 50km), Toulouse (up to 50km), Bordeaux (up to 50km), Lyon (up to 50km)
Experience
- TF1Data Engineer / Data Platform ArchitectPRESS AND MEDIASeptember 2024 - Today (1 year and 9 months)Boulogne-Billancourt, FranceContext:Assisting TF1 in the design, implementation, and industrialization of its Data & Cloud platform to meet governance, performance, and scalability challenges.Main Contributions:đ Data & Cloud Architecture:
- Design of architecture on Azure & Databricks aligned with business and technical needs,
- Execution of POCs and benchmarks to select the most suitable solutions.
- Microsoft Fabric: testing and implementation of a prototype for migrating SQL workloads to Fabric, including table migration, data transformation, and performance optimization in a modern Data Lakehouse architecture.
âïž Infrastructure as Code:- Deployment of Terraform modules to industrialize the platform:
- Implementation of a Unity Catalog module (Databricks) for data governance.
- Definition of access rights via a module: Databricks Access Matrix.
đ CI/CD & Automation:- Creation of CI/CD pipelines for automated deployments (Terraform & Databricks Bundles).
đ€ Collaboration & Best Practices:- Support for Data & BI teams on best practices (governance, security, performance).
- Upskilling internal teams on new tools: Terraform & Spark
⥠Data Optimization:- Improvement of Databricks processing workflows.
- Continuous optimization of the platform to enhance reliability and efficiency.
Results:- Implementation of arobust, secure, and governed Data & Cloud platform, adapted to the strategic challenges of a major media group.
- TotalEnergiesAWS/Azure Senior Data EngineerENERGY AND UTILITIESJune 2023 - August 2024 (1 year and 3 months)Paris, FranceContext:Participation in a project to locate and track electric vehicle charging stations in real-time, to improve the driver experience.Main Contributions:đ Infrastructure as Code: setup of the Databricks environment (workspaces, IAM roles, integration with S3 and AWS EC2 compute) via Terraform.đ CI/CD & Industrialization: automation with Databricks Bundles to deploy clusters, configure jobs, and integrate Spark code (Python).đ Data Architecture: design and implementation of the Data Lakehouse with different layers (Bronze, Silver, Gold) to ensure data quality and governance.⥠Data Pipelines:Batch processing (Bash + Spark) executed nightly to populate the geolocation API tables,Real-time processing to instantly update station status.Result:Implementation of a robust, automated, and scalable data platform, enablingreliable geolocationandreal-time updatesof charging stations, serving electric vehicle drivers.
- AXAAzure Sr Data EngineerBANKING AND INSURANCEDecember 2021 - May 2023 (1 year and 6 months)Suresnes, FranceContext:Leading themigration of an on-premise Data Lake to Azure Cloud, with the design and implementation of a modernData Lakehousearchitecture to support large-scale analytical and business intelligence needs.Main Contributions:đ Data Ingestion & Integration: design and implementation of end-to-end pipelines with Azure Data Factory, Databricks & Spark for smooth and automated integration.đ Transformation & Reporting: development of transformation workflows and creation of reliable business views, ensuring high-quality reporting.đ Lakehouse Architecture: implementation of a Delta Lakehouse on Azure Databricks, ensuring data consistency, scalability, and governance.âïž DevOps & Industrialization: adoption of Azure DevOps practices (Pipelines, Releases) to automate deployments and strengthen team collaboration.⥠Performance Optimization: implementation of advanced techniques on Azure Databricks (Spark optimization, Z-Order, Vacuum, Delta Lake management) to improve processing efficiency and reduce compute costs.Results:Successful migration from an on-premise environment to ascalable and governed cloud platform.Accelerated deployments through CI/CD automation.Provision of a reliable architecture enabling quality analysis and reporting, while reducing operational costs.
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Education
- Master Big DataUniversité Jean Monnet2016Master 2 Big Data
- Cloudera Training & Spark and Hadoop Developer CertificateXebia2017Formation Cloudera & Certificat Spark and Hadoop Developer