Top Data Engineer Jobs in France

The Software Engineer will manage data ingestion, enhance cloud infrastructure, collaborate on AI data roadmaps, and seek new audio data sources.

The Software Engineer will handle data collection for model training, manage cloud infrastructure on GCP, and collaborate with AI scientists to enhance data quality and efficiency. Responsibilities include sourcing audio data, extending ingestion pipelines, and planning for future datasets.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Lead SRE for Edenred's Data & AI platform: define and operate SRE standards, observability, FinOps, incident and service management; co-own CI/CD and architecture; act as player-coach to improve reliability and run critical systems.

Build and operate self-hosted, offline-capable infrastructure for data pipelines and machine learning environments. Responsibilities include Linux server and VM administration, Docker containerization, ML lifecycle platform management, dataset and artifact versioning, orchestration, CI/CD automation, monitoring, troubleshooting, and resource optimization. The role integrates data pipelines with training and inference systems while supporting large heterogeneous datasets and documenting platform operations.

Design, build, and operate high-volume streaming and batch ingestion pipelines for cardiac device data in a regulated HealthTech environment. Own workflow orchestration, data modeling, warehousing, quality, traceability, observability, deployments, incident response, and on-call operations. Collaborate with Analytics, Product, and Engineering teams while contributing to technical decisions and modernizing the orchestration platform with Temporal.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.

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.

The Data Engineer Intern will assist in building data pipelines, creating relational databases, and supporting data-driven solutions in the space sector.

Own and scale the ingestion pipeline and backend abstractions for high-throughput AI tracing data. Optimize ClickHouse schemas and queries, operate Langfuse Cloud and self-hosted deployments, and build infrastructure-heavy features that enable product engineers to ship data-heavy capabilities.

Founding Data Engineer to design, build and scale AI-first data pipelines and production systems for LLM-powered insurance workflows. Own ingestion, structured extraction, document processing, evaluation/monitoring, APIs and reliability to deliver production-grade AI products used by brokers.

Lead architecture and development of a low-latency trading stack focused on data infrastructure and control workflows. Design high-performance datastores, build a data platform for quantitative research, create control tools to safeguard operations, own systems end-to-end, and mentor engineers while collaborating closely with researchers.

Design, build, and operate high-frequency, event-driven data pipelines into Snowflake; implement reverse ETL; model data with dbt; deploy cloud infra as code; monitor and improve production reliability, latency, quality, and cost; mentor teammates and establish data engineering standards.

Design, build, and maintain a scalable data platform and ELT pipelines; model data in Snowflake; ingest from PostgreSQL, Elasticsearch and third-party APIs; power BI and product analytics; ensure data quality and governance; collaborate with stakeholders and mentor other engineers.

Design and implement a scalable, real-time data architecture to ingest public data sources, map exposed secrets to organizations, and analyze impact. Lead production deployments, monitoring, and maintenance while mentoring engineers, collaborating with backend/ML/Data teams, and driving roadmap and architectural decisions.

Design, build, and operate scalable data services and pipelines (real-time and batch) powering recommendations, search, and product-facing APIs. Evolve cloud data infrastructure, IaC/CI-CD, observability, and Snowflake data models; integrate AI/LLM features and collaborate across product, backend, and data science teams.

Design, build, and maintain streaming ingestion pipelines and migrate SaaS pipelines to in-house infrastructure. Deliver resilient, idempotent data into Snowflake, improve AWS streaming stack, manage IaC with Terraform, monitor and troubleshoot production pipelines, and collaborate on safe data access for AI tooling.

The Lead Data Engineer will develop data pipelines, support industrialization efforts, collaborate with teams, and engage in community events.

Lead and manage a data engineering team to develop and maintain data pipelines, collaborating with data scientists and ensuring code quality in agile environments.

Design and implement scalable features, build data pipelines, collaborate with cross-functional teams, and enhance data infrastructure for improved decision-making.