Top Data Engineer Jobs in France

The Senior Data Engineer will manage offline and online ML architecture, building data pipelines, and ensure low-latency feature processing while collaborating with data scientists and engineers.

The Senior Data Engineer will build and maintain data pipelines, distributed ETL/ELT workflows, and data storage systems while collaborating with various teams to optimize data quality and performance.

The Data 360 Solution Engineer/Architect collaborates with clients on solutions for data and analytics, guiding from presales to deployment, leveraging Salesforce tools and platforms.

Design and implement scalable features, build data pipelines, collaborate with cross-functional teams, and enhance data infrastructure for improved decision-making.

The Senior Data Engineer will build scalable data pipelines, lead technical initiatives, enforce data governance, and collaborate with multiple teams to enhance the data platform at TheFork.

Lead development of core geospatial products, data-processing and annotation tools. Set technical vision for web apps, ETL pipelines, and backend services; optimize ML model orchestration; mentor engineers; collaborate with product and customers to deliver scalable, near-real-time geospatial data systems.

The AI Data Engineer designs and operates data pipelines for reliable AI systems, focusing on data curation, evaluation workflows, and collaboration with engineering teams to ensure data integrity and performance efficiency.

Join the Platform engineering team to ensure the reliability and scalability of the Data Layer for the Shift Cloud Platform, focusing on automating deployment and managing Infrastructure-as-Code for AWS services whilst optimizing data platforms for performance and reliability.

As a DevOps Engineer, you will ensure the reliability, scalability, and performance of database platforms, automate operations, and support Kubernetes adoption, while collaborating with engineering and data teams.

Responsible for data collection to support model training, finding new audio data sources, and operating the cloud infrastructure for ingestion pipelines.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

The Data Scientist / AI Engineer will identify AI use cases, design and maintain LLM systems, deploy models, and support product teams with insights.

The Lead Data Engineer at Ipsen Pharma will define and standardize data engineering practices, build tools for automation, and lead community engagement while ensuring consistent solutions across teams.

As a Senior Analytics Engineer, you will build and maintain data pipelines, develop data marts and dashboards, and ensure data quality, while collaborating with stakeholders to meet their data needs.

The Senior Data & AI Engineer will build and maintain data pipelines, develop data models, automate workflows, and support GenAI implementations to enhance marketing solutions and data insights.

As a Senior Data Engineer at QRT, you will build scalable Data Lakehouse solutions, design software in the AWS cloud, develop data pipelines, and ensure data quality for trading desks.

As a Data Scientist apprentice, you will identify and deliver AI/machine learning use cases, build models, and integrate them into workflows.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

The AI and Data Operations Engineer will develop Python scripts, manage data lake infrastructure, support AI agents, and optimize processes.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

The Data Engineer will design and maintain data pipelines, optimize ETL processes, ensure data quality, and collaborate in an agile environment.

Design and maintain data infrastructure, build pipelines for data collection, ensure data quality, and collaborate with stakeholders on data requirements.