Top Data Engineer Jobs in France

Lead digital transformation initiatives for Airbus Manufacturing Engineering Work Preparation. Responsibilities include deploying unified platforms, defining data analytics strategy, governing data, implementing automation and AI solutions, ensuring export-control compliance, integrating manufacturing data, and coaching transnational teams. The role requires manufacturing engineering experience, project leadership, change management, AI and data expertise, stakeholder management, and willingness to travel.

Owns processing and technical quality of MRI data from raw acquisitions through quality-controlled datasets. Builds and maintains MRI processing and QA/QC pipelines, monitors acquisitions across imaging centers, investigates data-quality issues, and improves collection processes. Collaborates with MRI physicists, clinical research associates, and model developers to adapt pipelines and validate new sequences and protocols across sites.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

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.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain large-scale data pipelines, transform raw location and behavioral data into reliable features, and create datasets for products, analytics, and machine learning. Explore complex data, develop metrics and visualizations, monitor quality, investigate anomalies, and translate exploratory analyses into production systems. Collaborate with product, engineering, and data teams to improve data infrastructure and product intelligence.

Build and improve data infrastructure, data lakes, pipelines, analytical systems, and machine-learning services. Source, store, process, and analyze large datasets reliably and securely; integrate multiple data sources; and deploy scalable, production-ready services using models developed by data scientists. The role also requires learning unfamiliar datasets quickly and collaborating across international teams in ambiguous environments.

Design and improve data platforms, tools, and standards that enable engineering teams to build and operate data pipelines autonomously. Develop AI tooling, contribute to batch and event-driven architectures using Airflow, dbt, and Kafka, and promote platform adoption across teams in France and Brazil. The role also involves mentoring, documentation, cross-team collaboration, and shaping data engineering practices.

Build and improve data platform tools, standards, and infrastructure that enable engineering teams to own and operate data pipelines. Develop AI tooling, support batch and event-driven architectures using Airflow, dbt, and Kafka, establish engineering best practices, and help teams adopt platform capabilities across France and Brazil.

Build, maintain, and improve Alan’s data platform and infrastructure. Develop reliable data pipelines, support secure data warehouse access, strengthen data governance, and establish standardized data practices. Partner with technical and non-technical teams to deliver trusted data solutions, improve developer experience, and support international expansion. The role involves operating production infrastructure using orchestration, cloud warehouses, infrastructure as code, Python, SQL, and related tools.

Design, build, and operate data infrastructure to process massive event volumes into reliable, low-latency analytics and data products. Own real-time and batch services, platform tooling, observability, cost and performance optimization, on-call incident response, and cross-team stakeholder collaboration to enable analytics, ML, and reporting at scale.

Gather client requirements and architect, develop, and deploy cloud data platforms, warehouses, ETL processes, pipelines, APIs, and BI solutions. Perform advanced SQL and data modeling, support deployments and integrations, diagnose technical issues, mentor staff, contribute to innovation and practice development, and assist with project management. The role requires approximately 20% overnight travel and primarily in-person work.

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.

Data engineering internship with responsibilities and qualifications not specified in the provided description. The posting highlights Amadeus’s commitment to diversity, inclusion, accessibility, and recruitment safety.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Build and scale distributed data ingestion services for Algolia’s Connectors Platform. Responsibilities include improving reliability, scalability, throughput, testing, observability, and performance; operating high-traffic production systems; and mentoring teammates. The role works with Go, Kubernetes, Terraform, and Google Cloud to support data connectivity, transformation, and AI enrichment pipelines.

Develop and validate propulsion system models, analysis tools, and post-processing workflows for spacecraft development and testing. Collaborate with pressure and feed system engineers to design representative test setups, create test plans, analyze real-time and post-test data, correlate fluidic models, and resolve anomalies during test campaigns. Produce test reports and actionable performance insights while supporting propulsion subsystem design maturation and verification.

Own analytics for consumer sales and marketing across e-commerce, marketplaces, wholesale, and reseller channels. Develop dbt and Snowflake data models, maintain Tableau dashboards, measure marketing performance, reconcile sales data, and evaluate pricing and campaign impact using causal inference. Model customer lifetime value, improve data quality, support cross-functional integrations, use AI-enabled workflows, and present actionable recommendations to senior stakeholders.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Design, build, operate, and optimize large-scale Spark data pipelines for data lake ingestion involving hundreds of terabytes. The role uses Databricks, Snowflake or AWS Redshift, AWS cloud services, and Delta Lake or Apache Iceberg. Responsibilities include performance and cost optimization, lakehouse architecture, secure data management, governance, compliance, observability, and infrastructure practices. The engineer will collaborate within Odaseva’s R&D team to develop secure, scalable data platform capabilities.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

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.

Own electrical engineering and project delivery for energy infrastructure serving data centres and industrial customers. Responsibilities include managing development programmes and budgets, grid connection processes, electrical design, consultant and contractor coordination, design reviews, risk management, procurement, commissioning, energisation and handover. The role requires MV/HV infrastructure experience, GB or European grid connection expertise, multidisciplinary design coordination and accountability across first-of-a-kind power projects.

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.