Top Data Engineer Jobs in France

Six-month intern project exploring how large language models can extract reliable structured data from synthetic medical reports. Responsibilities include generating synthetic PDFs, testing prompting and fine-tuning approaches, evaluating model accuracy, robustness, speed, and cost, structuring extracted information in databases, developing AI data-processing workflows, documenting findings, and communicating recommendations to technical and clinical stakeholders.

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 a reusable Python ingestion framework to replace legacy Java ETL, develop real-time PostgreSQL-to-Snowflake streaming and CDC pipelines, and improve customer onboarding with LLM-assisted schema mapping. Own production deployment, monitoring, data quality, recovery, and secure handling of sensitive data. Collaborate with the Data and AI Platform team to design infrastructure and make pragmatic architectural decisions.

Leads the design, development, deployment, and operations of enterprise data platforms and scalable batch, streaming, and real-time pipelines on Google Cloud. Mentors data engineers, establishes engineering standards, and ensures data solutions are secure, governed, reliable, observable, scalable, and cost-efficient. Responsibilities include orchestration, CI/CD, infrastructure as code, production support, data quality, metadata management, stakeholder collaboration, and evaluation of AI and generative AI capabilities for engineering automation and platform improvement.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Own and scale HyLight’s client insights platform, processing terabytes of LiDAR, imagery, and methane data into customer-facing web applications and dashboards. Improve reliability, speed, data quality, observability, scalability, and cost efficiency across cloud and potential in-house infrastructure. Work end to end from backend and data pipelines to user interfaces, partner with operations and embedded teams, and apply AI tools to workflows such as data QA and incident triage.

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.

Build and maintain data models, ingestion pipelines, and analytics infrastructure using Python, SQL, Snowpark, PySpark, Dagster, Snowflake, and AWS. Own data topics end to end, collaborate with business stakeholders to define metrics, monitor pipeline operations, investigate data issues, and ensure code quality through testing and documentation. The role also supports internal AI tools and requires working onsite three days per week.

Run and improve the reliability of Veepee’s Kubernetes-based data platform, including SLOs, observability, infrastructure as code, GitOps, CI/CD, incident response, and capacity optimization. Collaborate with data teams on production readiness and migrations, while developing AI-assisted operational tools such as incident-investigation agents, MCP integrations, and automated runbooks.

Design and maintain scalable cloud data pipelines, data models, ETL processes, and machine learning workflows for digital pharmaceutical R&D. Ensure data quality, governance, observability, privacy, and compliance while supporting MLOps, CI/CD, containerized deployments, and AI-ready data products. Collaborate with technical and business stakeholders, coordinate deliverables, contribute to engineering standards, and provide technical guidance to team members.

Own the secure, scalable administration and governance of enterprise data platforms, especially Snowflake and AWS. Design RBAC, identity, access, masking, tagging, audit, and cost-optimization controls. Automate provisioning and compliance workflows with Terraform, Python, CI/CD, Okta, and SCIM. Administer Tableau and Alation, support SOX controls, and establish governance for Snowflake AI and machine learning capabilities. Partner with security, compliance, engineering, and governance stakeholders to improve platform maturity and self-service access.

Build interactive data visualizations, maps, dashboards, and 3D experiences that turn complex energy data into clear narratives. Work independently with marketing, policy, press, leadership, and data science teams using React, Next.js, Mapbox, WebGL, and Blender. Own projects end to end, select appropriate visualization techniques, communicate insights effectively, and rapidly adapt to varied project requirements.

Support ML engineers by improving the MLOps platform, implementing data science algorithms in client applications, collaborating on infrastructure, and deploying algorithms safely and scalably. The role involves Python backend development, data stores, CI/CD, Kubernetes infrastructure, monitoring, and production support. Candidates should be pursuing a Master's degree in Engineering and seeking an end-of-study internship.

Owns production data integrations for an AI-assisted KYC and customer due diligence platform. Responsibilities include mapping data sources, defining data contracts and canonical models, building connectors, integrating Databricks with SQL Server and legacy systems, supporting APIs and SFTP workflows, implementing data-quality checks and contract tests, and validating reliable production data flows.

Design and maintain scalable cloud data pipelines, data models, ETL/ELT processes, data warehouses, ingestion workflows, and machine learning data workflows. Ensure data quality, governance, monitoring, and pipeline reliability while collaborating with business and technical stakeholders. Apply engineering best practices including CI/CD, containerized deployments, infrastructure as code, code reviews, and technical documentation. The role also requires strong SQL, Python, cloud, orchestration, and LLM experience.

Design and maintain scalable cloud data pipelines, data models, ETL processes, machine learning workflows, and data governance practices. Translate business needs into technical solutions, ensure data quality and observability, automate monitoring, and promote engineering standards including CI/CD, containerization, and code reviews. Mentor team members and collaborate with international technical and business stakeholders in a biopharmaceutical digital transformation environment.

Design and scale omics data architecture, ingestion pipelines, data models, lineage, storage layouts, and access layers. Ensure processed sequencing and molecular data is reliable, reproducible, and accessible to computational biologists and research models. Collaborate with data engineering and computational biology teams on databases, cloud storage, infrastructure, CI/CD, and model-ready datasets. Support the evolution of a large oncology data corpus and develop understanding of omics processing and bioinformatics workflows.

Designs, develops, deploys, and monitors machine learning, deep learning, generative AI, LLM, RAG, and agentic AI solutions. The role covers predictive modeling, feature engineering, experimentation, MLOps, LLMOps, explainability, model optimization, and AI pipeline integration with enterprise systems. The Data Scientist collaborates with engineering and business teams, promotes responsible AI practices, evaluates emerging technologies, and serves as a technical reference for data science and AI engineering.

Build and operate Python backend services and AI-agent pipelines that crawl, process, validate, and index legal documents. Design retry-safe workflows, APIs, database schemas, migrations, lineage tracking, evaluation gates, and observability. Collaborate with legal experts and engineering teams to onboard new data sources, improve extraction quality, control processing costs, and resolve production failures.

Own the technical transformation of Finance by building data platforms, pipelines, models, dashboards, and internal tools. Migrate spreadsheet workflows to reliable code-based solutions, establish data quality and governance standards, partner with Finance and Data teams, and identify AI and automation opportunities. Support Finance users through documentation, training, and ongoing collaboration.

Design, operate, and scale highly available Kubernetes platforms across multi-region environments. Improve reliability, resilience, performance, observability, and SRE practices across Data & WebCloud services. Automate infrastructure and deployments using Terraform, Ansible, GitOps, and CI/CD. Troubleshoot critical incidents, conduct root-cause analysis, and collaborate with engineering, database, networking, and observability teams on distributed architectures and production reliability.

Research data-centric methods that improve humanoid robot learning. Responsibilities include designing multimodal datasets, data curation and selection tools, training-stage data composition, controlled ablations, scaling studies, and real-robot evaluation. The role connects research findings to data collection strategy and reproducible training workflows, collaborating with AI, robotics, infrastructure, and data engineering teams.

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.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Designs, builds, deploys, and supports production AI, data, and software solutions for clients. Responsibilities include writing production code, architecting systems, building data pipelines and AI workflows, implementing testing and monitoring, driving user adoption, and guiding engineers. The role also involves client engagement, technical scoping, stakeholder communication, project estimation, candidate interviewing, and development of reusable internal AI capabilities.