About Abdelaziz
Senior Data Engineer with 5+ years of experience in consulting (KPMG), delivering high-impact data projects for major clients such as Chanel, Danone, Crédit Agricole, and Royal Canin.
I specialize in designing modern data architectures (Azure, Databricks, Microsoft Fabric) and building robust, scalable data pipelines (ETL/ELT), helping organizations turn their data into actionable insights and business value.
I also support companies in data transformation initiatives, including platform migrations (Lakehouse architecture), CI/CD implementation, and performance & cost optimization.
Recently, I have been working on Generative AI use cases, designing and deploying RAG (Retrieval-Augmented Generation) architectures to build intelligent assistants leveraging internal document repositories.
👉 My goal: to help businesses unlock the full potential of their data through reliable, scalable, and ROI-driven solutions.
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km)
Experience
- CHANELData Engineer / Data Apps DeveloperLUXURY GOODSFebruary 2026 - Today (4 months)Paris, France
- Designed and developed Data Applications (Data Apps) focused on data entry, governance, and business user adoption.
- Built end-to-end solutions combining frontend interfaces (Streamlit / Dash / Gradio), business logic, and data pipelines within the Databricks ecosystem.
- Modeled and implemented robust, scalable, and governed data structures using Unity Catalog.
- Developed and orchestrated data ingestion, synchronization, and exposure pipelines across the Lakehouse architecture.
- Implemented advanced data security strategies, including Row-Level Security (RLS) with Lakebase (PostgreSQL).
- Delivered high-quality user-centric interfaces (UX/UI) to ensure strong adoption by business teams.
- Developed advanced Python components: data processing, internal APIs, and application logic.
- Industrialized data workflows with CI/CD, logging, monitoring, and observability frameworks.
- Produced detailed architecture documentation, data models, and engineering guidelines.
- Supported testing, deployment, and production release processes, ensuring reliability and scalability.
- Royal CaninData Engineer / PMOAGRICULTUREApril 2023 - January 2026 (2 years and 9 months)Paris, FranceAs part of an international program in the veterinary field, Royal Canin provides a digital platform used by veterinarians around the world during consultations. This platform allows veterinarians to enter patient information (cats and dogs), such as health status, weight, nutrition, dietary recommendations, and consultation follow-ups, with the aim of improving animal welfare and personalizing nutritional recommendations:
- Analysis and mapping of data sources and definition of tables required for dashboards.
- Design and development of ETL pipelines with Azure Data Factory, reducing the processing time for data from multiple sources.
- Optimization of performance on Azure Databricks, with significant improvements in the speed of computing jobs.
- Implementation of use cases as part of cost reduction for the client.
- Migration and adaptation of existing pipelines to Databricks (Unity Catalog).
- Design of analytical tables for Power BI consumption.
- PMO role in addition to the Data Engineer position.
- Close collaboration with business teams to define new tracking and functional improvement requirements for the platform and anticipate the data impacts of application developments.
- Active participation in project management using agile methodology, with 4-week sprints.
- Progress monitoring, stakeholder coordination, prioritization of data issues.
- Key role in communication between different teams, ensuring alignment between business needs, technical constraints, and project planning.
- Supervision of junior consultants.
- KPMGAI Engineer (Generative AI / RAG)CONSULTING AND AUDITSJanuary 2025 - June 2025 (5 months)Paris, France
- Designed and developed an intelligent assistant based on RAG (Retrieval-Augmented Generation) to support internal auditors in accessing and analyzing large volumes of documentation.
- Built an end-to-end pipeline including document ingestion, chunking, embedding generation, and semantic retrieval.
- Implemented vector search and similarity matching to enable accurate and context-aware responses.
- Integrated LLMs to generate reliable, explainable answers grounded in internal knowledge bases (PDFs, reports, guidelines).
- Improved information retrieval efficiency, significantly reducing time spent searching across internal documents.
- Designed evaluation approaches to assess answer relevance, faithfulness, and context quality.
- Collaborated with business users to align the solution with real operational needs and ensure adoption.
- Contributed to defining best practices for Generative AI use cases in enterprise environments.
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Education
- Complex System EngineeringÉcole Centrale Marseille2021Complex System Engineering
- Engineering DegreeEcole Centrale Casablanca2021Engineering Degree