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.

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.

Own HyLight’s end-to-end flight data chain, from payload readiness and field data offload to cloud ingestion and customer deliverables. Ensure data reliability, investigate hardware, network, and software incidents, optimize cloud and connectivity costs, automate manual workflows, and build operational health and cost metrics. The role spans embedded systems, field operations, data pipelines, cloud infrastructure, and AI-enabled workflow improvement.

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.

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.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

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 optimize batch and streaming data pipelines, ETL/ELT processes, data models, and analytics solutions on Google Cloud. Support Vertex AI and Gemini-based ML solutions, MLOps, prompt engineering, embeddings, and RAG experimentation. Participate in client requirements gathering, architecture implementation, data governance, quality validation, security, monitoring, and documentation while collaborating with senior engineers and architects.

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 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 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 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.

Lead the architecture and development of a multi-country Snowflake and GCP data platform. Build CDC ingestion pipelines, dbt semantic models, and DataOps CI/CD workflows; implement governance, RBAC, PII masking, compliance, FinOps, and performance optimization. Partner with business stakeholders, document architectural decisions, and mentor data engineers across LATAM operations.

Develop and deploy scalable, production-grade machine learning and AI solutions for financial research. Build LLM- and NLP-based applications, collaborate with senior engineers, and contribute to reliable AI-driven products. Responsibilities require Python, software engineering, deep learning, databases, Docker, and at least one major deep learning framework. Preferred experience includes RAG pipelines, vector databases, LLM fine-tuning, cloud platforms, and Kaggle competitions.

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.

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.

Designs and maintains scalable cloud data pipelines, data models, ETL processes, machine learning workflows, and AI-ready datasets. The role focuses on data quality, governance, observability, monitoring, CI/CD, containerized deployments, and technical standards. It collaborates with business and technical stakeholders to translate requirements into solutions while applying generative AI technologies and supporting Sanofi’s commercial digital transformation.

Build and deploy an AI platform feature involving LLMs and agentic workflows for fraud detection or payment integrity. Responsibilities include researching approaches, designing solutions, developing and integrating features, evaluating precision, recall, and latency, and deploying systems to production with cross-functional teams.

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.