We are looking for a Site Reliability Engineer (SRE) to join Lucidya Cloud Engineering team and contribute to improving the reliability, scalability, and automation of our cloud-based infrastructure. The ideal candidate will have hands-on experience with cloud environments, containerized workloads, automation tools, and monitoring systems, as well as a proactive mindset for enhancing system availability and performance.
Infrastructure Reliability
Ensure high availability (HA) and scalability of critical infrastructure components (e.g., Redis, RabbitMQ, Kubernetes clusters).
Proactively identify and eliminate single points of failure across the cloud environment.
Linux Systems Administration: Handle infrastructure management tasks such as patching, performance tuning, and monitoring of Linux-based systems.
Cloud Operations
Manage and optimize cloud-based workloads across AWS, GCP, or Azure.
Automate provisioning, scaling, and maintenance tasks using Infrastructure as Code (IaC) tools such as Terraform, AWS CloudFormation, or similar.
Kubernetes Clusters
Manage the day-to-day operations of Kubernetes clusters, including deployment, scaling, upgrades, and troubleshooting.
Monitoring and Incident Response
Implement and standardize monitoring solutions using tools like Datadog, Prometheus, or Grafana to track golden metrics and improve alerting systems.
Participate in on-call rotations, troubleshoot incidents, and drive post-incident reviews to implement lasting solutions.
Automation and Scripting
Develop and maintain automation scripts for routine operational tasks to reduce manual efforts and increase efficiency.
Advocate for AWX/Ansible adoption to automate configurations and deployments.
Collaboration and Best Practices
Work closely with DevOps and Engineering teams to identify and resolve performance bottlenecks.
Contribute to the establishment of best practices for infrastructure and application reliability.
Experience and Knowledge
~ 3 years of experience in a similar SRE, DevOps, or Infrastructure Engineer role.
Strong experience with at least one major cloud provider (AWS, GCP, or Azure).
Hands-on experience with Kubernetes and containerization (e.g., Docker).
Technical Skills
Proficient in scripting languages such as Python, Bash, or similar for automation.
Familiarity with Infrastructure as Code (IaC) tools like Terraform, Pulumi, or AWS CloudFormation.
Strong understanding of load balancers, networking (IP management, subnetting), and HA architecture.
Experience with CI/CD tools (e.g., Bitbucket Pipelines, Jenkins, GitHub Actions).
Monitoring and Observability
Experience with modern monitoring and observability tools (e.g., Datadog, ELK, Grafana).
Ability to define and track golden metrics and establish meaningful alerting thresholds.
Problem Solving and Troubleshooting
Strong analytical skills and ability to resolve complex technical issues.
Proven track record in root cause analysis and incident management.
Soft Skills
Excellent communication and collaboration skills to work across teams.
Self-motivated and proactive in improving systems and processes.
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