Top Data Engineer Jobs in Lille

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.

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 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.

Design, develop, and optimize scalable data pipelines and ETL processes on GCP or AWS. Manage SQL/NoSQL databases, ensure data quality and interoperability, apply CI/CD and DevOps practices, and collaborate with data scientists and analysts to support business needs.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Design and build highly scalable, low-latency market data systems and APIs. Implement backend services, influence architecture, maintain test coverage, participate in code reviews, troubleshoot incidents, and occasionally be on-call. Work spans streaming and historical market data endpoints and developer-focused API improvements.

Design, build, and scale low-latency market data systems and public APIs. Influence architecture, implement backend services, maintain quality and tests, participate in reviews, troubleshoot incidents, and optionally serve on-call.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

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

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

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

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Build and maintain high-performance market data infrastructure in Rust to ingest, process, store, and serve real-time pricing feeds. Solve latency, data quality, and cross-exchange consistency problems, contribute trading/market-data expertise to engineering decisions, communicate with a distributed team, and potentially lead a small team over time.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead design and ownership of a cloud-native data platform: build scalable batch ETL and ingestion pipelines, develop analytical models (Athena/Redshift/ClickHouse), integrate data workflows with microservices on ECS, ensure data quality/observability, performance tuning, implement IaC and CI/CD, and mentor engineers.

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.

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

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

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.

Lead migration from Snowflake to the client platform, build AI-native data migration capabilities, design and maintain scalable data pipelines and platforms, propose improvements, and collaborate with senior engineers, product/project managers, and data analysts.

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.