Top Data Engineer Jobs in France

Build, operate, and improve Alan’s data platform and production data infrastructure. Responsibilities include developing data pipelines, enabling secure warehouse access, strengthening data security and governance, standardizing data practices, supporting engineers and analysts, and scaling infrastructure for international expansion. The role requires collaboration across technical and non-technical teams, with a focus on reliable, secure, and developer-friendly data systems.

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.

Design, optimize, and maintain scalable data architectures, pipelines, and processing solutions. Ensure data quality, perform SQL and Python analysis, build dashboards and visualizations, and support marketing and product analytics. Lead tooling development, requirements gathering, QA, stakeholder communications, and cross-functional initiatives while providing technical guidance and managing team decisions.

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 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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

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.

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.

Build and improve a scalable data platform, including ingestion, transformation, warehousing, orchestration, governance, observability, and CI/CD. Audit the existing stack, establish data engineering standards, improve pipeline reliability and cost control, and deliver trusted datasets for product analytics, data features, and ML/AI use cases. Collaborate with Product, business teams, and company leadership on architecture decisions and strategic data initiatives.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

Own Filigran’s Enterprise Data Platform end to end, including ingestion pipelines, dbt-based data modeling, quality testing, observability, governance, and Metabase dashboards. Maintain and troubleshoot Airbyte and Airflow pipelines, develop trusted KPI models, and enable self-service analytics across business teams. Collaborate cross-functionally, improve platform architecture, and independently deliver reliable data products in a remote, async-first environment. Bonus initiatives include AI agents, LLM integrations, semantic layers, and proactive data alerting.

Build and maintain Azure-based data pipelines, warehouses, Data Vault and dimensional data marts using ADF, Azure SQL, and T-SQL. Develop Power BI datasets, semantic models, and reports; optimize stored procedures; investigate source systems; and implement data quality monitoring. The role also involves profiling datasets, validating business results, debugging data platforms, and collaborating with clients and a remote European team.

The posting identifies a Senior Data Engineer focused on analytics. The provided description contains only recruitment-template instructions and does not specify responsibilities, qualifications, technologies, or other role details.

Designs, builds, and maintains scalable data pipelines and products using Azure and Databricks. Responsibilities include ELT/ETL development, workflow orchestration, data quality and observability, testing, monitoring, CI/CD, performance and cost optimization, production troubleshooting, documentation, and stakeholder collaboration. The role requires strong Python and SQL skills, modern data engineering experience, data governance knowledge, and French and English proficiency.

Lead the design, implementation, and operation of enterprise reporting and analytics platforms. Build scalable pipelines, master data structures, lakehouse and warehouse models, Power BI semantic models, dashboards, and cost-rate databases. Ensure data quality, governance, security, lineage, and auditability while supporting DevOps, deployment, monitoring, and production operations. Partner with finance, operations, and senior stakeholders to deliver trusted data products and business outcomes across complex enterprise systems.

Own end-to-end scientific data pipelines from ingestion through trusted, model-ready datasets. Model compounds, structures, assays, predictions, and experimental results while ensuring data quality, lineage, governance, and observability. Partner with ML engineers, data scientists, and researchers to build scalable solutions, shape data architecture, develop self-service and API capabilities, and mentor junior engineers.

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.

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.

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.

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.