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Description

Hello 👋

I am Illiyas,Data Expert/ **Data Engineer**.

9 years ago, I built my first data pipeline.

Since then, I've spent nearly a decade building data systems

that process millions of events per day.

I have migrated entire architectures without downtime.

I have debugged Airflow DAGs that were in an infinite loop at 3 AM.

I have seen Kafka partition in any way possible.

I have optimized Spark jobs that took 6 hours to run, reducing them to 20 minutes.

In 2017, I dove deep into data engineering.

Not by chance. By conviction.

Because I understood that the companies that would dominate

the next decade would be those capable of transforming

their data into intelligent systems.

Not those that store it. Those that EXPLOIT it.

Today, I no longer code just for the sake of coding.

I build data architectures that:

→ Scale from 1K to several million events/day without redesign

→ Run in production without waking up the team at night

→ Cost 40% less thanks to cloud optimization

I transform your data infrastructures into strategic assets.

My concrete achievements:

- Snowflake → AWS/Databricks Lakehouse Migration (RATP): -30% costs, +50% performance, migration of 100+ dbt models

- Real-time prediction platform (La Poste): +25% accuracy, -15% logistics costs, several million packages/day

- Scalable Data Mesh Architecture for RATP Group: time-to-market divided by 3

- Regulatory DataLake (Natixis): 0 downtime in production, critical 24/7 environment

My expertise:

- Modern data architecture (Data Lakehouse, Data Mesh, Lambda Architecture)

- Real-time streaming and distributed batch processing

- Distributed processing (ETL, ELT, ...)

- MLOps and AI in production (deployment, monitoring, CI/CD)

- Microservices and containerization

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

RATP
Data Architect
TRANSPORTATION
June 2023 - Today (3 years)
Paris, France
Building the Group Data Platform that centralizes all RATP data projects and allows business teams to access data services in self-service.

Scaling the platform for adoption by 15+ teams in 18 months.

Key Achievements:
→ DataMesh Architecture enabling 50+ Data Engineers, Ops, and Business users to access data in a decentralized and governed manner
→ Implementation of a batch and real-time ingestion pipeline processing millions of events/day (Spark, Kafka, S3)
→ Deployment of microservices with automated CI/CD (GitLab)
→ Reduction of data project deployment times by 40% through automation
→ Training and upskilling of 20+ Data Engineers on the Spark ecosystem and architecture best practices

Technical Stack:AWS, Spark, Scala, Databricks, S3, Data Mesh Architecture, GitLab CI/CD, Terraform
AWS Snowflake DBT Scala Databricks
Geopost
Lead Data Engineer
LOGISTICS AND SUPPLY CHAIN
April 2021 - June 2023 (2 years and 2 months)
Paris, France
Developing from scratch a real-time platform for detecting delayed packages based on Machine Learning, enabling anticipation of logistical issues before they impact customers.

Result:35% reduction in delivery delays through predictive detection.

Technical Achievements:
→ Architecture and deployment of a real-time ingestion pipeline processing 2M+ events/day (Spark Streaming, Kafka, Airflow)
→ Full platform containerization on Kubernetes
→ Deployment of ML models with real-time monitoring and alerting
→ End-to-end fully automated CI/CD (GitLab, GitLabHQ)
→ Optimization of Spark jobs: 60% reduction in execution time and 40% in cloud costs

Stack:Spark • Kafka • Airflow • Cassandra • S3 • Kubernetes • Docker • GitLab • JupyterHub • Scala • Python • Java

Business Impact:Anticipation of delays • Improvement of customer satisfaction • Reduction of
Docker Kubernetes Python Spark Airflow
Artifeel
Data Engineer
PUBLIC SAFETY
January 2021 - March 2021 (2 months)
Paris, France
Designing and implementing a Cloud/Azure architecture.

Project:Deployment of an Azure architecture for detecting abnormal behavior in time series using Machine Learning models.

Achievements:
- Setting up a data integration pipeline (development and scheduling via Azure Functions),
- Securing access and data (Azure Key Vault),
- Preparing the development environment for building Machine Learning models (Azure Machine Learning),
- Monitoring the actual daily consumption of resources used.

Result:Installation of a cloud architecture.

Technical Stack:MS Azure (ADLS Gen2, Functions, Key Vault, Machine Learning).
MS Azure Python Cloud Integration Cloud Administration Infrastructure as Code

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Degree in Aeronautical and Space Systems Engineering
Institut Polytechnique des Sciences Avancées
2010
Specialization Certificate in Massive Data Processing
Conservatoire National des Arts et Métiers
2017

Check out Illiyas's education

Microsoft Azure: Fundamentals
Microsoft
2023
GCP: Associate Cloud Engineer
Google Cloud Platform
2023

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