Top Data Engineer Jobs in France

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.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

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.

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 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.

Build and ship end-to-end features for Akeneo Supplier Data Manager across data-processing pipelines, backend services, and TypeScript/React frontend systems. Help process large, messy supplier catalogues using deterministic workflows and LLM-based extraction and classification. Own features through production monitoring and support, participate in customer discovery, collaborate across the Product Cloud, and develop engineering skills through pairing, code review, and mentorship.

Design, build, and operate Akeneo’s Supplier Data Manager at enterprise scale. Develop reliable data-processing pipelines for messy supplier catalogues, including deterministic and LLM-powered extraction and classification. Create collaboration and governance workflows, ensure production observability and operational excellence, participate in customer discovery, and collaborate across teams. The role requires strong software engineering fundamentals, architectural thinking, debugging ability, end-to-end ownership, and clear communication.

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.

Design, build and maintain scalable data models, ETL pipelines, and data access layers. Collaborate with product and data science teams to enable analytics and dashboards, participate in agile processes, code reviews, deployment, and monitoring to deliver production-quality data solutions.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

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 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.

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

Owns the full lifecycle of pricing data products, including data pipeline ownership, architecture, production-quality coding, and ML system infrastructure. Collaborates with data scientists and cross-functional teams to deploy real-time pricing algorithms, scale ML infrastructure, optimize performance, and build enterprise data warehouses, marts, dashboards, and KPI metrics. The role also manages orchestration, cloud infrastructure, DevOps, data governance, GDPR compliance, and stakeholder communication.

Design and build an in-house, self-managed data platform and complex pipelines for structured, image, video, sensor, and time-series data. Responsibilities include offline-first ingestion, resumable transfers, reconciliation, data modeling, storage planning, orchestration, validation, quality monitoring, traceability, and ML data preparation. The role requires robust Python and SQL development, Linux administration, architecture ownership, and collaboration with machine learning, DevOps, and software engineering teams.

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.

Build and maintain production data pipelines for AI systems, from ingestion and data capture through monitoring. Ensure data quality, privacy, anonymization, consent, retention, and GDPR compliance. Provide datasets for model training, evaluation, and annotation workflows, while developing monitoring pipelines and dashboards for AI performance. Collaborate with data platform, data science, engineering, product, and business teams to optimize scalable cloud infrastructure and manage costs.

The Senior Data Engineer will build and maintain data pipelines on GCP for AI models, ensuring data quality and collaborating with teams on data governance.

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.

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.

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.

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.

Build and maintain analytics-ready data models using dbt and SQL, implement data quality checks, and develop official Sigma dashboards. Help establish shared metric definitions, documentation, and data governance across the merged group. Automate data workflows using AI coding tools and agents, support AI-powered self-service analytics, and bridge technical data work with business users.