We are seeking a skilled Kubernetes Engineer to support our growing data science infrastructure, which includes containerized notebooks (e.g., JupyterHub), dashboards (e.g., Grafana, Streamlit), and databases (e.g., PostgreSQL, ClickHouse, MongoDB). You will play a key role in ensuring high availability, performance, and security of our Kubernetes-based platform that enables our data scientists and analysts to do impactful work at scale.

Key Responsibilities:

Design, deploy, and maintain Kubernetes clusters tailored to data science workloads.

Manage and optimize containerized applications including Jupyter notebooks, dashboards, and analytics tools.

Deploy and maintain managed and self-hosted databases in Kubernetes (e.g., using Helm, Operators).

Automate infrastructure provisioning and scaling using tools like Helm, Kustomize, and Terraform.

Implement monitoring and logging for platform reliability (e.g., Prometheus, Grafana, Fluentd).

Collaborate with data scientists and ML engineers to understand their workflows and improve usability and performance of the platform.

Ensure security best practices are followed for authentication, secrets management (e.g., with Vault or Sealed Secrets), and role-based access control (RBAC).

Troubleshoot system, networking, or container issues affecting data science workflows.

Qualifications:

Required:

3+ years of hands-on experience with Kubernetes in a production environment.

Strong understanding of containers and Docker.

Experience managing and scaling applications in Kubernetes using Helm or Kustomize.

Experience with cloud providers (AWS, GCP, or Azure), preferably with managed Kubernetes (e.g., EKS, GKE, AKS).

Familiarity with CI/CD pipelines for infrastructure and application deployment (e.g., GitOps).

Solid Linux systems administration skills.

Understanding of networking, load balancing, DNS, and security concepts in containerized environments.

Preferred:

Experience supporting JupyterHub, MLflow, Airflow, or similar data science/ML tools.

Knowledge of managing PostgreSQL, ClickHouse, or NoSQL databases in Kubernetes.

Familiarity with dashboarding or data visualization platforms (e.g., Grafana, Streamlit, Superset).

Experience with data science and machine learning workflows.

Proficiency in scripting languages like Bash or Python.

Soft Skills:

Strong problem-solving skills and ability to troubleshoot complex system issues.

Effective communicator who can collaborate across engineering and data science teams.

Self-motivated and eager to learn emerging tools and technologies.

Ability to document infrastructure and support runbooks clearly.

Why Join Us?

Work in a data-driven company empowering real-time decision-making.

Influence the design of a modern data platform from the ground up.

Collaborate with top-tier data scientists and engineers.

Flexible work environment with support for remote collaboration.