Site Reliability Engineer
Databricks5 months ago
San José, Costa RicaSenior
Responsibilities
- Design and deploy production-grade cloud infrastructure using Terraform or Pulumi.
- Optimize system performance, architecture, scaling, uptime, and latency for critical IT services.
- Architect CI/CD pipelines and manage hosted and self-hosted GitHub runners.
- Build infrastructure that enables secure applications with logging, metrics, and alerts by default.
- Develop internal AI plugins and automation scripts to improve developer workflows and operational efficiency.
- Participate in on-call rotations, lead incident response and troubleshooting, create service-health dashboards, and facilitate blameless post-mortems.
- Collaborate with Security, Engineering, and Support teams on infrastructure and operational outcomes.
Requirements
- At least 5 years of production-level software engineering experience is required.
- Strong proficiency in Python is non-negotiable.
- Expert-level proficiency with Terraform or Pulumi is required, including modules and state management.
- Hands-on experience with AWS, Azure, or GCP, plus Kubernetes and Docker, is required.
- Experience with observability tools such as Datadog, Prometheus, or ELK is expected.
- Proficiency with distributed systems concepts such as Kafka or messaging queues is required.
- Advanced knowledge of GitHub Actions and GitHub Runners is required.
- Candidates should be able to own ambiguous projects and execute independently with minimal guidance.
Benefits
- Comprehensive benefits and perks are offered, with details varying by region.
Tech Stack
Apache KafkaApache SparkAWSAzureDatadogDockerGitHub ActionsGoogle Cloud PlatformKubernetesMLflowPrometheusPythonTerraform
Categories
DevOpsSite Reliability
About Databricks
Databricks builds a cloud-based data and AI platform centered on the lakehouse architecture, combining data engineering, analytics, and machine learning with Apache Spark, Delta Lake, and MLflow. It sells subscriptions and cloud services to enterprises that need to unify data pipelines and develop large-scale AI and analytics. Founded in 2013 by the creators of Apache Spark and headquartered in San Francisco, the company is privately held and serves organizations across many industries.