Is the Senior Data Engineer role at Vizzia remote?

Yes, the Senior Data Engineer role at Vizzia is a remote position.

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Senior Data Engineer

VizziaRemote3h ago

🎯 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
Cloud: AWS (S3, Lambda, RDS, Step Functions), GCP
Containerization: Docker
Observability: Datadog, Grafana, Prometheus
MLOps: MLflow, SageMaker