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# Data Engineer Modern Data Stack H/F

- **Company:** Orange Group
- **Location:** Lyon, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Cloud Computing, Continuous Integration, Data Warehousing, Github, Python (Programming Language), Azure Machine Learning, SQL Databases, Talend, Parquet, AWS Cdk, Google Cloud, Azure Data Factory, Large Language Models, Snowflake, Apache Spark, Code Structure, HybridCloud, Git, Data Lakes, Pyspark, Gitlab-ci, Bicep, Apache Kafka, Apache Nifi, Machine Learning Operations, Terraform, Azure Synapse Analytics, Jenkins, Amazon Redshift, Databricks
- **Published:** July 21, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/81472494.html

## About the Role

Expérience : au moins 5 ans sur des projets de data engineering, dont une expérience significative sur la mise en production de pipelines en environnement cloud.

-Python : maîtrise confirmée (code structuré, testé, packagé), ainsi que SQL avancé.

-Intégration & transformation : dbt, Spark / PySpark, Talend, Kafka / Kafka Connect, Nifi, Airbyte ou équivalents - plusieurs outils du marché pratiqués.

-Orchestration : Airflow, Dagster, Prefect, Azure Data Factory.

-Cloud : maîtrise d'au moins une des plateformes (Azure, GCP, AWS) avec une expérience significative sur ses services data (BigQuery, Synapse, Databricks, Snowflake, Redshift...).

-IaC : Terraform, AWS CDK, Bicep.

-CI/CD : GitHub Actions, GitLab CI/CD, Jenkins.

-Data/AI (un plus) : connaissance des plateformes MLOps, services AI/ML cloud, formats et moteurs de table (Parquet, Delta Lake, Iceberg).

-Cloud hybride : expérience des déploiements et intégrations avec des infrastructures on-premise., Contrôle qualité
SQL
AWS
Git
Batching
Jenkins
Python
KAFKA

## Description

Concevoir, développer et maintenir des pipelines d'ingestion et de transformation de données (batch et temps réel) en Python et SQL.

-Mettre en oeuvre des Modern Data Platforms sur le cloud (Azure, AWS, GCP) : Data Lake, Data Warehouse, zones raw / cleansed / business.

-Industrialiser les traitements : orchestration, CI/CD, tests, reprise sur erreur, déploiement via IaC.

-Mettre en place les contrôles de qualité de données et l'observabilité associée (fraîcheur, complétude, lineage, alerting).

-Contribuer aux projets liés à la donnée et à l'IA (LLM chatbot, RAG, Azure AI/ML services) en exposant des données fiables et exploitables.

-Optimiser les performances et les coûts des traitements (partitionnement, formats defichiers, dimensionnement, FinOps), y compris sur des architectures hybrides intégrant des environnements on-premise.


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