Top Data Engineer Jobs in France

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.

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 design and delivery of shared observability data platform capabilities to reduce developer friction, improve operational visibility, enable cost intelligence, build self-service operational tooling, drive reusable services, mentor engineers, influence cross-team architecture, and participate in on-call to improve platform reliability.

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.

Build and maintain Databricks-based Lakehouse pipelines (batch and near-real-time) using PySpark. Design Delta Lake schemas, ensure pipeline reliability and cost efficiency, write tests, and collaborate with product and data teams to enable data enrichment and serving for internal and client products.

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.

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.

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.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

The Data Engineer will build and maintain data infrastructure, collaborate with stakeholders, design data models, and implement data quality checks to support financial operations.

Design, build, and maintain scalable data pipelines. Optimize workflows on Databricks and Snowflake, ensuring data security and governance while preparing for AI/ML use cases.

Design, build, and operate ingestion, curation, versioning, and delivery pipelines that convert terabytes of raw, unstructured multimodal field and public data into training-ready datasets. Align multimodal streams, define labeling requirements, enable reproducible dataset lineage, optimize storage and I/O for model training, and provide tooling for dataset construction and loading.

Design and build production data pipelines for place and location datasets. Work with large, noisy geospatial data, improve dataset quality and observability, define data-quality metrics, prototype in Python/SQL notebooks, integrate experiments with backend systems, and run offline evaluations and A/B tests.