🎯 Mission

Vizzia develops technology solutions with strong societal impact, combining artificial intelligence and field observation to better understand and manage environmental challenges.

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.

🛠 Responsibilities

• 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).

✅ Profile

Technical skills

• 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, Airflow

• Cloud: AWS (S3, Lambda, RDS, Step Functions), GCP

• Containerization: Docker

• Observability: Datadog, Grafana, Prometheus

• MLOps: MLflow, SageMaker