Top Data Engineer Jobs in France

As a Data Engineer, build and optimize high-performance data infrastructures for deep learning by managing large volumes of raw video data, ensuring efficient processing pipelines and data quality.

As a Data Analyst, you will analyze data to drive insights, create reports and dashboards, and collaborate with teams to optimize product performance.

As the Data Engineer, you will design and maintain data pipelines, scale data infrastructure, ensure data quality, and collaborate with analysts to enhance decision-making capabilities.

Build and maintain GTM systems and data pipelines: design data models, develop internal FastAPI/React apps, lead an OpenAI Agent-based GTM AI layer, drive architecture and CI improvements, perform code reviews, and collaborate with Sales, RevOps, Data, and R&D to increase adoption and reliability.

The Clinical Data Engineer will manage data pipelines for clinical studies, ensuring data integration and analysis for hardware validation and product accuracy. The role involves close collaboration across teams and requires expertise in data engineering, statistical modeling, and biometrics.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Build and operate scalable ETL/ELT pipelines to ingest, transform, and model data across Palantir Foundry and Databricks. Ensure data quality, lineage, observability, and access controls. Collaborate with analysts and product teams to translate requirements into reliable, documented datasets and establish standards, monitoring, and automation for production data flows.

Build and operate Vibe's petabyte-scale data platform: storage, compute, orchestration, and OLAP reporting. Optimize pipelines for cost and latency, run on-call and incident response, write alerts/runbooks, and partner cross-functionally to deliver reliable, performant data services for product, ML, and finance teams.

Own and operate production reporting pipelines: implement schema changes, move and transform tables, ensure accurate advertiser reporting, troubleshoot discrepancies, and collaborate with Data Platform to flag scaling or data-quality risks.

As a Data Engineer, you will develop data pipelines, collaborate with data scientists, and deploy cloud infrastructures, while participating in community events.

Lead data engineering for ML-driven NiDAR: ensure complete recordings, build/manage multi-site data collection pipelines and databases, handle annotations and metadata extraction, ensure regulatory compliance, and prepare datasets for models (classification, tracking, sensor fusion).

Lead technical direction and hands-on implementation of semantic infrastructure for telemetry interoperability. Build scalable systems bridging OpenTelemetry, cloud and customer data models; define standards, mentor engineers, run on-call rotations, and drive cross-team platform initiatives for schema evolution and AI-enabled observability features.

Design, build, and maintain scalable ETL/data pipelines and foundational datasets for product, marketing, and finance. Ensure data quality through validation and monitoring, optimize infrastructure for scale, and collaborate with analysts and engineers. Integrate AI tools to improve workflows and automation.

Support the data team to build and maintain dbt data models, create Metabase dashboards, run data quality checks, and collaborate with teams to enable data-driven decisions.

The Data Engineer will develop data pipelines, collaborate with data scientists, deploy cloud infrastructure, and contribute to community knowledge.

The Data Engineer will develop data platforms in the cloud, create data pipelines, collaborate with data scientists, and deploy infrastructures using infra-as-code.

As a Data Engineer, you will develop data pipelines, support data scientists, deploy cloud infrastructure, and collaborate within the data community.

Develop data pipelines, support data scientists, deploy cloud infrastructures, and engage in community events. Requires strong data engineering skills and collaboration.

As a Data Engineer, you will develop data pipelines, support data scientists, deploy cloud infrastructure as code, and participate in the data community.

Lead data engineering efforts, manage data governance, ensure compliance, and oversee data platform economics while designing scalable systems.

Join as the first data-hire to grow the Data Warehouse: design and build ETL/ELT pipelines, model payment and product data, automate processes with Python, create dashboards, and implement data validation tests while gathering requirements from business teams.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Design, implement, and industrialise Azure-based data platforms (Data Lake/Warehouse/Lakehouse). Build ETL/ELT pipelines, data models, ensure data quality/security, collaborate with BI teams, run workshops, draft technical documentation, and support pre-sales and DataOps/DevOps practices.

Design, develop, and optimize scalable data pipelines and ETL processes on GCP or AWS. Manage SQL/NoSQL databases, ensure data quality and interoperability, apply CI/CD and DevOps practices, and collaborate with data scientists and analysts to support business needs.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.