Top Data Engineer Jobs in France

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.

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

Build and own scalable Azure data platforms, SQL Server and Databricks environments, end-to-end pipelines, and maintainable data models. Apply Kimball dimensional modeling, solve complex client data problems, challenge unclear requirements, and communicate technical concepts clearly to technical and non-technical stakeholders. The role emphasizes production reliability, client impact, continuous learning, and collaboration in a fully remote European data consultancy.

Own data pipeline workstreams and cloud data warehouse solutions; develop performant data models, ETL/ELT processes, and machine learning workflows. Implement data quality, governance, monitoring, observability, CI/CD, containerized deployments, and infrastructure-as-code practices. Partner with cross-functional teams to translate business needs into technical delivery plans, troubleshoot production issues, and improve engineering standards through documentation, reviews, and knowledge sharing.

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.

Lead data engineering initiatives across large-scale e-commerce datasets. Own data models, transformation architecture, analytics projects, client data products, internal dashboards, documentation, and technical roadmap delivery. Collaborate with technical and business stakeholders, mentor teammates, and translate business needs into scalable data solutions using SQL, BigQuery, Google Cloud, ETL/ELT tools, and visualization platforms.

As a Software Engineer, you will manage data collection, enhance cloud infrastructure for data ingestion, and collaborate with AI Scientists to improve data quality and efficiency.

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.

Two-year Airbus Global Graduate role developing expertise as a Technical Data Wiring Engineer. Responsibilities include defining, designing, manufacturing, testing, and maintaining aircraft electrical systems; learning wiring processes, tools, and customer services; supporting digital and operational improvements; and rotating through manufacturing ground testing, electrical system design, technical data publication, and technical data programs.

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 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.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Maintain and improve Doctolib’s cloud-native Data and AI Platform, including the Data Product Controller and Dagster orchestrator. Enable autonomous data product deployment through CI/CD and infrastructure as code, while monitoring reliability, performance, costs, data quality, lineage, and observability. Collaborate with data engineers, data scientists, and product stakeholders to resolve infrastructure issues and improve platform scalability, architecture, and developer experience.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Design, build, and operate batch and streaming data pipelines; move production data into data lakes; ensure dataset quality, freshness, and observability; optimize queries and processing for correctness, latency, and cost; prototype and integrate production experiments; build reusable derived datasets.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

The Senior Python Developer will build and enhance data platforms for quantitative trading, ensuring code quality and performance while collaborating with teams.