Freelancer profile translated to English.

Description

Senior Data Engineer & Cloud Data Architect AWS | GCP (7+ years of experience)

Is your data platform becoming difficult to maintain, are your pipelines unstable, or are your cloud costs exploding?

I intervene to audit, secure, or redesign your data platform on AWS and GCP, in order to deliver a high-performing, scalable, and cost-controlled architecture, without relying on temporary fixes.

For over 7 years, I have been supporting SMEs, large companies, and data teams on high-stakes projects: IT system migration, data platform modernization, pipeline industrialization, and cloud optimization.

My role: transform a fragile data platform into a reliable and usable foundation, with a particular focus on performance, governance, and cost control.

Among my interventions:

→ Data & Cloud Architecture Audit (AWS/GCP) with clear, prioritized, and actionable reporting

→ On-premise data migration to cloud (AWS & GCP)

→ Modern data architecture design (Bronze / Silver / Gold, Lakehouse)

→ Industrialization of batch & near real-time data pipelines

→ FinOps optimization with observed cost reductions of up to -40%

My approach

A pragmatic and impact-oriented approach:

a stable, documented, and scalable data foundation that allows your teams to work with peace of mind without constantly fixing the same problems.

Stack & methods:

AWS & GCP (S3, Glue, EMR, Athena, ECS, EKS, BigQuery, GCS, Composer, GKE, Dataproc, Cloud SQL)

Spark, Airflow, SQLMesh, Kafka

Python, SQL, Scala

Terraform, Docker, Kubernetes, CI/CD

Data Lake, Lakehouse, DWH

FinOps: compute & storage optimization, autoscaling, spot, partitioning

Do you need to secure a data platform, conduct an audit, or temporarily strengthen your data team?

Are you hesitant about the solidity or the real cost of your data platform?

Contact me, I offer a video call to give you a clear, jargon-free initial diagnosis.

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent
Arabic
Native or bilingual

Workplace preferences

Can work on-site

Lyon (up to 50km)

EDF
Expert Data Engineer
ENERGY AND UTILITIES
September 2025 - Today (9 months)
Lyon, France
Information system transformation and industrial maintenance mission within the framework of the Vision 2035 program, with a strong focus on migration to a trusted cloud, data pipeline reliability, and data governance.

Main responsibilities:

- Design and development of robust and industrialized data pipelines (batch & near real-time)
- Participation in the migration and modernization of data flows (Oracle / PostgreSQL)
- Implementation of large-scale data processing (Spark, SqlMesh)
- Integration and orchestration of data workflows (GCP Composer, AWS Glue / GCP DataProc)
- Contribution to data governance (quality, traceability, repositories)
- Production of reliable datasets for business intelligence reporting (Power BI)
- Close collaboration with business, data, and architecture teams within an Agile framework

Technical environment:
- AWS: S3, Glue, DMS, RDS, ECS, KMS
- GCP: GS, DataProc, Composer, DMS, Cloud SQL, GKE
- Other: CI/CD, PowerBI.
Google Cloud Amazon Web Services Apache Airflow Apache Spark PySpark
Lizeo
Senior Data Engineer | Cloud Data Architect | AWS Expert | FinOps & Cost Governance Strategy
AUTOMOBILE
October 2019 - September 2025 (5 years and 11 months)
Lyon, France
Redesign of the data architecture to support the international growth of a key player in the data sector in the tire industry.

🎯 Mission Objectives:

• Handle a significant increase in load due to exploding collected data
• Design a scalable and reliable architecture (data warehouse, data lake, data lakehouse)
• Reduce cloud costs through a structured FinOps approach

🚀 Key Achievements:

• Creation of a scalable matching engine, based on business rules co-defined with tire experts (replacing an obsolete Java engine)
• Implementation of a modern data architecture combining data lake (S3), data warehouse (Athena), and data lakehouse to unify storage and analysis.
• Deployment of a high-performance Big Data stack: Spark, Kafka/MSK, EMR, EKS
• Advanced pipeline orchestration with Apache Airflow: reliability, real-time monitoring, automation
• Implementation of dashboards (Apache Superset) for proactive observability and faster decision-making
• Adoption of DevOps best practices: CI/CD with GitLab, Terraform, automated tests

📈 Concrete Business Results:

•💰 Reduction of ~40% in AWS cloud costs through FinOps (resource optimization, monitoring, alerting)
•⚡ Accelerated time-to-market: 1 day to integrate a new product line into workflows
•🛡️ Enhanced stability: only 2 critical incidents in 2024 on massive volumes
•🌍 Active contribution to technological watch and adoption of a robust data foundation for international operations

🧰 Technical Stack: Scala, Python, Spark, Kafka, Iceberg, Airflow, AWS (EMR, Glue, S3, Athena, EKS, Lambda), Terraform, GitLab CI/CD, Superset, Data Warehouse, Data Lake, Data Lakehouse

📅 Methodology: Scrum 🧩
Amazon Web Services Python Kubernetes Scala Apache Spark