Freelancer profile translated to English.
Back to original languageAbout Clément
Data Engineer & Cloud Data Architect| I help B2B SaaS companies scale their data infrastructure and ensure pipeline reliability | GCP & AWS ⚙️
Is your product growing, your data volumes exploding, but your tech stack can't keep up? Jobs are failing, dashboards are slowing down, and every new feature puts your pipelines under strain?
I step in to structure and automate your data infrastructure on GCP and AWS, make your pipelines reliable and scalable, and enable you to grow without technical debt or performance loss.
My goal: a stable, high-performing data platform ready to support your growth.
With over 5 years of experience, I have supported:
- pass Culture: audited and optimized the GCP stack (BigQuery, Airflow, DBT), improving reliability and scalability.
- Club Med: deployed a data architecture on GCP, automated with Terraform, and integrated AI pipelines (RAG, LangChain, Vertex AI).
- Livetrend: designed a cross-cloud AWS ↔ GCP infrastructure for ingesting and exposing production data.
- namR: industrialized a GCP infrastructure (GKE, Airflow, CloudSQL) and automated Computer Vision pipelines.
This diversity allows me to understand your product and data challenges and deliver concrete, operational solutions quickly.
My approach: get straight to the point to design reliable and scalable architectures rapidly. Every technical choice is guided by performance, simplicity, and maintainability.
Technical Stack:
GCP (BigQuery, Vertex AI, Cloud Run, GKE, Dataform), AWS (Aurora, ECS, EFS), Python, DBT, Airflow, PostgreSQL, Terraform, Docker.
Do you want to improve your pipeline reliability, scale your data stack, and build a solid foundation for your growth?
Contact me for an audit call — response within 1 hour.
French
Native or bilingual
English
Native or bilingual
Can work on-site
Paris (up to 50km), Marseille (up to 50km), Lyon (up to 50km), Bordeaux (up to 50km), Nantes (up to 50km)
Experience
- NaptaData Architect & Cloud Engineer AWSSOFTWARE PUBLISHINGNovember 2025 - November 2025Paris, France🎯 Context :Assessed the scalability of Napta's analytical architecture to address performance limitations encountered by its largest clients.Objective: Conduct an in-depth audit of the current data stack, confirm the robustness of critical components, identify performance levers, and explore new structural evolution paths.
- Comprehensive audit of the existing data architecture: analysis of the denormalized model, identification of theoretical bottlenecks, architectural limitations, and technical constraints.
- Validation of current pipeline performance: benchmarking of the existing solution, load analysis, and confirmation of short-to-medium term scalability.
- Exploration of new target architectures, modeling alternatives, and progressive evolution strategies, considering costs, timelines, and complexity.
✅ Impact :- Confirmation of the robustness of the existing analytical architecture.
- Reduced risks related to scalability.
- Identification of potential limitations of the current model.
- Opened new avenues for evolution adapted to product growth.
🧰 Technical Stack :- Cloud & Data : AWS Lambda, AWS RDS, PostgreSQL
- Data Engineering : Python, SQL, Data Modeling, Performance Optimization
- Architecture & Scalability : Data Architecture, Bottleneck Analysis, Scalability Strategies
- RewardPulseCloud Data Backend Engineer | NestJS | Typescript | GCPSOFTWARE PUBLISHINGNovember 2025 - December 2025 (1 month)Paris, FranceI supported RewardPulse in strengthening its data-driven backend by developing new features within theirNestJS/TypeScript/GCParchitecture. I contributed to structuring the code, improving business logic, and ensuring the reliability of existing systems while respecting the scalability constraints specific to their platform.Key achievements:
- Development of newNestJS backend APIs(modules, services, controllers).
- Integration and optimization of the data layer with Prisma & PostgreSQL.
- Contribution toCloud ArchitectureonGCP(Cloud Run, CloudSQL, Cloud Functions).
- CI/CD via GitLab for a more reliable release cycle.
- Infrastructure as Code via **Terraform**.
Stack: **NestJS**, **TypeScript**, **Prisma**, PostgreSQL, GCP, Terraform, GitLab CI. - pass CultureSenior Data Engineer GCPCULTUREJune 2025 - December 2025 (6 months)Paris, France🎯 Context :Structuring and optimizing the data infrastructure with the following objectives: audit and optimize the data stack, improve reliability and security, reduce cloud costs, accelerate pipeline scalability, and implement best DataOps practices.
- Comprehensive audit of the existing data stack (GCP, BigQuery, Airflow, DBT).
- Implementation of a FinOps strategy on BigQuery → 54% reduction in storage costs.
- Optimization of the GKE infrastructure (Airflow) → 86% cost reduction.
- Knowledge transfer and upskilling of the Data team.
✅ Impact :- Significant optimization of GCP costs.
- Improved data reliability through testing.
- More scalable and robust infrastructure.
🧰 Technical Stack :- Cloud & Data: GCP (BigQuery, GKE, Cloud Logging Sinks)
- Data Engineering: Airflow, DBT, Python, FastAPI
- Infrastructure as Code: Terraform, Terragrunt
- DevOps & Orchestration: ArgoCD, CI/CD (GitHub Actions)
- BI: Metabase
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Education
- Applied Machine Learning in Python Applied Plotting, Charting & Data Representation in Python Orthodidacte MOOCApplied Machine Learning in Python Applied Plotting, Charting & Data Representation in Python Orthodidacte MOOC
- Engineering DegreeEcole Centrale de Marseille2022Diplôme d'ingénieur