Bio
I design, build, and operate the cloud infrastructure that turns data complexity into scalable advantage. My approach is simple: start with the business need, build for reliability, and make sure teams can own what we deliver.
I've worked across multiple industries, in international environments and alongside teams spread across multiple regions and time zones.
What I deliver:
• Multi-cloud data platforms on AWS and Alibaba Cloud, from architecture to production
• Lakehouse architectures with access management, governance, and cross-regional compliance
• Infrastructure as Code and CI/CD pipelines for data platform teams
• Backend development and internal APIs to support data ingestion and platform operations
• Architecture advisory and strategic guidance for C-level and technical leadership
• Technical training and team upskilling on cloud and data engineering
I partner with engineering teams and organizations ready to build reliable, scalable data infrastructure. The goal is always the same: platforms that work in production, and teams equipped to evolve them.
My coworkers' feedbacks
Experiences
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Air liquide
Senior Cloud & Data Platform Engineer
Jan 2025 — Dec 2099
Context:
Contributing to Air Liquide's digital transformation through the Unified Data Platform (UDP) serving 75+ countries. Working across two major streams: multi-cloud data platform engineering and R&D initiatives.
▸ Data Platform — Alibaba Cloud (APAC/China)
- Replicating lakehouse capabilities on Alibaba Cloud for APAC/China operations
- Architecture design, PoC, and cross-functional collaboration with regional teams
- Data integration and migration strategies across cloud providers
- Data residency, compliance frameworks (APAC/China regulations), and risk management at global scale
▸ R&D — Alibaba Cloud / Qwen
- Proposing an alternative to group-level Gemini Apps for Mainland China using Qwen (Alibaba Cloud Model Studio)
- Exploring custom RAG architecture on Alibaba Cloud to integrate internal company data with Qwen LLM
▸ Data Platform & Infrastructure — AWS
- Migrated from AWS Lake Formation to stable authorization system ensuring business continuity
- Maintained Infrastructure as Code (CloudFormation, CDK) for lakehouse environment (Glue, Athena, Lake Formation, DataZone, SageMaker Unified Studio)
- Implemented secure inter-account AWS access (Data Platform ↔ ML platform) via external trust and S3 unload
- Developed backend solutions (Python, FastAPI, boto3) for data ingestion and governance
- Created internal APIs for data container provisioning and DataZone access management
- Designed secure dataset preview solution bypassing Lake Formation limitations while preserving healthcare/HR data confidentiality
- Implemented CI/CD pipelines (GitLab CI, Docker, PyDoit, Poetry)
- Deployed API Gateway with Cognito authentication, VPC integration, and granular IAM permissions
- Conducted PoC leveraging dlt with AWS Batch to simplify the ingestion layer and reduce operational costs
Business Impact:
Improved platform reliability and scalability, enabled secure cross-platform data exchanges, expanded capabilities to new regions.
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Doctrine
DataOps Engineer
Sep 2023 — Jan 2025
Context:
Worked at France's leading legal intelligence platform serving over 1 million monthly visitors and 14,000 legal professionals. Doctrine centralizes and organizes legal information to help lawyers and legal experts better advise their clients while making law more accessible and transparent.
Key responsibilities and achievements:
- Managed SalesOps infrastructure: administered and optimized back-office systems supporting client operations and internal teams
- Actively contributed to migrating critical systems (back office, APIs, Airflow, databases, AWS) to more resilient and scalable architectures
- Designed and implemented batch and real-time pipelines for processing legal and commercial data
- Deployed Infrastructure as Code solutions with comprehensive monitoring
- Developed JavaScript/Python applications and established CI/CD pipelines
- Created technical documentation and modernized legacy code
- Actively participated in technical workshops and architecture sessions related to internationalization projects
Technical skills:
Python, JavaScript, Node.js, SQL, PostgreSQL, Docker, Git, Linux, AWS (S3, EC2, RDS, Lambda, CloudWatch), Apache Airflow, Terraform, CI/CD, Metabase, RabbitMQ, REST APIs/Postman, Chargebee, Slack, CRM, Data enrichment, Software engineering, Cloud computing, DevOps
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Stellantis
Data Engineer
Jan 2023 — Aug 2023
Context:
Contributed to a financial data management project for PSA Banque Stellantis, within Quantmetry, a consulting firm specializing in data and artificial intelligence.
Key responsibilities and achievements:
- Data pipeline design and maintenance: designed, developed, and maintained data ingestion, transformation, and exposure pipelines handling up to 1 TB of financial data
- Spark optimization: improved Spark processing performance through DAG analysis and Spark UI monitoring, reducing processing time by 30%
- Mentoring & team growth: supported junior engineers and interns on data lake environments, reviewed reports, and fostered a positive team dynamic
- Automated deployment: implemented automated pipeline deployments via TeamCity, enhancing deployment efficiency and reliability
- Operational maintenance: managed the operational maintenance of PSA Banque’s data lake (monitoring, incident handling and resolution, client communication)
Technical skills:
Java · Apache Spark · Python · SQL · Hive · Bash · HDFS · TeamCity · Git · Maven · Artifactory · Apache Kafka · Amazon S3 · Kibana · Agile methodologies · JIRA · Trello · Confluence
Team:
5 Data Engineers · 1 Tech Lead · 1 Project Manager
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Decathlon
Data Engineer
Nov 2021 — Jan 2023
Context:
Contributed to a margin control automation project for Decathlon, the world’s leading sports retailer. The objective was to prevent financial penalties by systematically comparing accounting data with cash register receipts.
Key responsibilities and achievements:
- Developed a large-scale ELT pipeline processing several million records each month
- Automated financial data comparison and made results accessible to analysts
- Pioneered the implementation of dbt on Databricks with Delta Lake format — the first squad to deploy this solution at group level
- Created training materials on dbt/Databricks and supported other teams
- Set up CI/CD workflows using GitHub Actions, and DevOps templates for PySpark and Airflow
- Optimized data pipelines through Z-ordering, Spark shuffle reduction, Databricks autoscaling, and seamless integration of Delta, dbt, and Qlik
- Developed workarounds for technical constraints, such as the absence of a data rejection mechanism during ingestion
- Achieved concrete results: reduced errors, faster decision-making, and significantly fewer penalties
Technical skills:
Python · SQL · PySpark · Databricks · Amazon S3 · Amazon Athena · AWS Glue · Apache Airflow · Git · Delta Lake · dbt · Qlik · Cloud Computing
Team:
2 Big Data Engineers · 2 Business Analysts · 1 Project Manager
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Quantmetry
Data Engineer, Trainer & Meetup Organizer
Sep 2021 — Sep 2023
Data Engineer – Stellantis / PSA Avtec Powertrain Pvt. Ltd.
January 2023 – August 2023
Data Engineer – Decathlon France
November 2021 – January 2023
Designed and delivered internal and client training sessions
Topics: Advanced Python, Clean Code, Apache Airflow, Apache Spark, dbt
Organized internal technical meetups
Speakers from Back Market, Nickel, OCTO Technology, Decathlon, etc.
Co-organizer of the Paris Airflow Meetup
Data engineering / Apache Airflow community
Co-organizer of the FranceAI Meetup Group
In collaboration with TotalEnergies and Hugging Face