Senior Data Engineer

We are looking for a Senior Data Engineer to optimize, secure, and industrialize the data pipelines powering our AI products. Your role is critical: you ensure the quality, availability, and performance of the data used daily by AI, Product, and DevOps teams., * Design, develop, and maintain robust, automated, and scalable data pipelines.

• Ensure data quality, security, and reliability throughout the entire data lifecycle.
• Define and maintain infrastructure as code for data-related services.
• Build and maintain dashboards, monitoring tools, and reports for internal teams.
• Work closely with Data Science, DevOps, and Product teams to ensure data consistency and value.
• Monitor and optimize performance using observability tools (Datadog, Grafana, Prometheus).

Do you have experience in SQL?, Do you have a Master's degree?, * Master's degree (or equivalent) in computer science, data engineering, or AI.

• 5+ years of experience in Data Engineering, ideally in cloud and AI-driven environments.
• Excellent command of Python and software engineering best practices (testing, versioning, packaging).
• Strong knowledge of SQL and NoSQL databases (PostgreSQL, DynamoDB).
• Solid experience with workflow automation (Airflow, GitHub Actions, GitLab CI/CD).
• Strong understanding of MLOps concepts, data integration into ML workflows, monitoring, and deployment.
• Cloud experience on AWS or GCP (S3, Lambda, RDS, Step Functions).
• Knowledge of Docker and containerized environments.

Soft skills

• Strong technical rigor and constant focus on quality.
• High level of autonomy and ability to own a broad scope.
• Clear, structured communication with a collaborative mindset.
• Ability to work with cross-functional teams.
• Analytical mindset and attention to detail.

What Will Make the Difference

• Proven experience running critical production data pipelines.
• Advanced practice of data observability (logs, metrics, alerting).
• Open-source contributions in the data or ML ecosystem.
• Proactive approach to continuous improvement of data workflows and environments.
• Sensitivity to the environmental or societal impact of technology.

Tech Stack

• Languages: Python
• Databases: PostgreSQL, DynamoDB
• Pipelines: GitHub Actions
• Cloud: AWS (S3, Lambda, RDS, Step Functions), GCP
• Containerization: Docker
• Observability: Datadog, Grafana, Prometheus
• MLOps: MLflow, SageMaker

Benefits

Hybrid work

"Contrat cadre" and RTT (between 8-12 per year depending on the number of public holidays in the current year)

A Mac or PC depending on your preferences