> Markdown version of [/jobs/ext/3190079-data-engineer-m-f](https://www.wearedevelopers.com/jobs/ext/3190079-data-engineer-m-f). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer (M/F) - **Company:** Magellan - **Location:** Lille, France - **Experience:** Experienced - **Salary:** €45,000.0 - €52,000.0 - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Big Data, BigQuery, Databases, Continuous Integration, Extract Transform Load (ETL), DevOps, Github, Python (Programming Language), NoSQL, Power BI, Software Engineering, SQL Databases, Data Streaming, Google Cloud, Data Management, Terraform, Data Pipelines - **Published:** September 1, 2026 - **Apply:** https://fr.indeed.com/viewjob?jk=d987c671fb793fa1 ## About the Role You already have solid experience of 4 years in Data within GCP/AWS cloud and DevOps environments. Solution-oriented, you are also a strong communicator and pedagogical with teams. Proficiency in programming skills (Python, SQL, etc.), database management, and software engineering. Knowledge of Big Data architectures is essential. You are curious and stay up to date on development methods, technologies, and architectures. You are fluent in French. ## Description Industrialising Data platforms (pipelines, ETL, quality, governance) on GCP or AWS cloud environments represents a structural workstream for data-driven organisations. This role targets a confirmed Data Engineer, capable of working in coordination with Data Scientists and Analysts teams., As a Data Engineer (M/F), you design and develop data pipelines within a demanding technical environment (SQL/NoSQL, GCP, BigQuery, PowerBI, Terraform, GitHub, Python). * Design and develop robust data pipelines: collection, processing, storage * Optimise data flows to improve efficiency and performance * Manage SQL and NoSQL database infrastructures in a GCP or AWS Cloud environment following CI/CD principles * Integrate data by developing ETL processes and ensuring interoperability between different systems and sources * Guarantee data quality: implement quality and consistency checks, cleansing and validation processes * Liaise with the entire Data team (Scientists and Analysts) to support and address business needs