Cloud Engineering Manager

We are looking for a skilled Cloud Engineering Manager to drive the strategic direction of cloud infrastructure and DevOps practices within our organization.

This position requires a blend of leadership technical expertise and operational excellence to oversee high-performing teams and deliver scalable reliable and resilient systems for mission-critical applications.

• Oversee the design and implementation of infrastructure and product monitoring systems
• Define and utilize SLI/SLO standards to strengthen reliability and system performance
• Conduct root cause analysis to identify and address system inefficiencies
• Facilitate postmortem analyses and drills to optimize incident response strategies
• Evaluate product performance scalability and reliability to ensure operational excellence
• Automate operational tasks to optimize workflows and improve productivity
• Deploy CI/CD pipelines and champion modern DevOps practices
• Manage cloud infrastructure and configuration through Infrastructure-as-Code tools
• Collaborate with cross-functional teams to align cloud strategies with business goals
• Support the growth of engineers through mentoring and professional development initiatives
• Plan and execute staffing strategies to build a scalable and efficient engineering team

• Minimum of 7 years of experience in cloud engineering DevOps or SRE
• Expertise in scripting languages such as Python Go Bash or PowerShell
• Proficiency in observability tools including Prometheus Grafana DataDog and ELK
• Background in cloud infrastructure management tools like Terraform and cloud-specific CLI tools (gcloud az aws)
• Skills in configuration management tools such as Ansible
• Knowledge of CI/CD platforms including Jenkins (Groovy SDK Jenkinsfile) GitLab-CI or Azure DevOps
• Expertise in containerization technologies such as Docker and Kubernetes
• Strong ability to analyze incidents and develop strategies for improvement
• Capability to design scalable cloud-native solutions in alignment with business objectives

• Familiarity with hybrid cloud environments and multi-cloud architectures
• Background in integrating machine learning workloads into cloud ecosystems
• Showcase of working with observability stacks tailored for microservices architecture
• Understanding of serverless cloud platforms and associated workflows