Freelancer profile translated to English.

Description

AI & Data Architect with over 13 years of experience in designing and deploying large-scale data platforms for international clients (Europe, America, Asia). Expert in Google Cloud Platform, BigQuery, Vertex AI, and LLMs (Gemini, Claude), I transform your data into strategic assets through cloud-native architectures, governed semantic models, and custom generative and agentic AI solutions. From DataLake to conversational AI assistants, I support companies in their data-driven transformation by designing scalable, performant, and directly actionable systems for the business. Accustomed to complex, multicultural, and high-stakes environments, I have led distributed teams of over 30 engineers and delivered strategic programs monitored at the highest level (Board, C-Level). My goal: to make data and artificial intelligence a concrete lever for growth and decision-making for your organization.

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

IBM
Data & AI Architect
E-COMMERCE
May 2022 - Today (4 years and 1 month)
Paris, France
End-to-end leadership of the design, implementation, and governance of a modern analytical data platform on Google Cloud Platform, in an international and multicultural context, with managerial and technical responsibility for a distributed team of 30 Data Engineers across multiple geographical areas (Europe, Asia, America).

Orchestration of the integration of heterogeneous data sources (third-party APIs, relational and NoSQL databases, flat files, real-time streams) within a centralized cloud architecture. Design and implementation of event-driven ingestion pipelines leveraging Cloud Pub/Sub, Cloud Functions, and Eventarc, ensuring real-time responsiveness, horizontal scalability, and optimal application decoupling between data producers and consumers.

Data Modeling in BigQuery
Building the analytical data model in BigQuery using a layered approach (staging, intermediate, mart) incorporating partitioned tables, materialized views, and cost/performance-optimized models. Definition of dimensional schemas and transformation rules to feed business use cases (reporting, ad hoc analysis, machine learning).

Artificial Intelligence & Generative AI

Design and deployment of AI assistants and intelligent agents leveraging Vertex AI, Gemini, and Claude, integrated with the semantic layer and BigQuery data model. Development of agentic solutions capable of querying data in natural language, generating automated insights and actionable recommendations directly usable by business teams (marketing, finance, operations). Implementation of RAG (Retrieval-Augmented Generation) pipelines connecting LLMs to the company's governed data, enabling non-technical users to make data-driven decisions without writing a single line of SQL.
Gemini Big Query Vertex AI Google Cloud Platform LLM
Société Générale
Lead Data Engineer
BANKING AND INSURANCE
September 2021 - May 2022 (8 months)
Paris, France
Technical domain lead for Big Data at DAAS
Transforming raw data into relevant indicators in collaboration with the Product Owner
Designing, developing, testing, and industrializing Big Data processing in various business and application contexts in collaboration with data architects and DevOps
Providing support for data processing
Defining and implementing best practices for data anonymization and pseudonymization in the DataLake (GDPR)
Industrializing, optimizing, and securing developments
Implementing and improving CI/CD processes (Jenkins, Kubernetes, Docker Enterprise)
Contributing to the Group's technical expertise communities

**Technical Environment**: Scala, Spark, Java, Spring Boot, Kafka, Apache Livy, HDFS, Presto, Trino, Docker, Kubernetes, Yarn, Hive, PostgreSQL, Azure Storage, Azure Kubernetes (AKS)
BPCE
Lead Data Engineer
BANKING AND INSURANCE
February 2019 - September 2021 (2 years and 7 months)
Paris, France
Leading the strategic migration of the On-Premise data ecosystem to Google Cloud Platform. Design, architecture, and deployment of a centralized DataLake structured in zones (raw, curated, refined) enabling massive ingestion of heterogeneous sources, automated data quality validation, as well as enrichment and transformation in batch via orchestrated pipelines. Implementation of governance, partitioning, and access control rules ensuring data reliability, traceability, and compliance at the organizational scale.

**Technical Environment**: Java, Scala, Spark, Python, Java, Terraform, Cloud Composer, BigQuery, Cloud Storage, Virtual Private Cloud, Cloud Dataproc, Jupyter, Zeppelin