Cloud Cost Optimization Engineer 17823
Veritaz ABPublicerad: 2026-05-22
Ansök senast: 2026-06-21
Beskrivning
Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry.
Assignment Description
We are currently looking for an experienced Cloud Efficiency Engineer
What You Will Work On
Analyze infrastructure usage and identify cloud optimization opportunities
Drive cost efficiency initiatives across cloud-native platforms
Optimize Kubernetes resource allocation and workload placement
Improve platform performance while maintaining service stability
Execute rightsizing and autoscaling initiatives
Troubleshoot resource contention and performance bottlenecks
Optimize compute utilization and infrastructure consumption
Analyze and improve JVM-based application performance
Implement measurable cloud cost reduction initiatives
Collaborate with engineering and data teams to support optimization programs
Strengthen operational efficiency through automation and platform improvements
Support long-term scalability and reliability objectives
What You Bring
Minimum 5 years of experience in cloud infrastructure engineering
Proven hands-on experience with cloud cost optimization and resource efficiency
Deep expertise in Google Cloud Platform or strong equivalent experience in AWS or Azure with readiness to work in GCP environments
Strong understanding of cloud compute models, pricing structures, and infrastructure utilization
Extensive experience optimizing Kubernetes / GKE workloads in large-scale environments
Experience with rightsizing, autoscaling, and troubleshooting resource contention
Strong Java knowledge and experience supporting JVM-based services
Practical experience with GC log analysis, heap tuning, and runtime optimization
Demonstrated ability to implement measurable optimization initiatives
Strong understanding of balancing performance improvements with reliability and stability requirements
Structured and data-driven approach to infrastructure decision-making
Assignment Description
We are currently looking for an experienced Cloud Efficiency Engineer
What You Will Work On
Analyze infrastructure usage and identify cloud optimization opportunities
Drive cost efficiency initiatives across cloud-native platforms
Optimize Kubernetes resource allocation and workload placement
Improve platform performance while maintaining service stability
Execute rightsizing and autoscaling initiatives
Troubleshoot resource contention and performance bottlenecks
Optimize compute utilization and infrastructure consumption
Analyze and improve JVM-based application performance
Implement measurable cloud cost reduction initiatives
Collaborate with engineering and data teams to support optimization programs
Strengthen operational efficiency through automation and platform improvements
Support long-term scalability and reliability objectives
What You Bring
Minimum 5 years of experience in cloud infrastructure engineering
Proven hands-on experience with cloud cost optimization and resource efficiency
Deep expertise in Google Cloud Platform or strong equivalent experience in AWS or Azure with readiness to work in GCP environments
Strong understanding of cloud compute models, pricing structures, and infrastructure utilization
Extensive experience optimizing Kubernetes / GKE workloads in large-scale environments
Experience with rightsizing, autoscaling, and troubleshooting resource contention
Strong Java knowledge and experience supporting JVM-based services
Practical experience with GC log analysis, heap tuning, and runtime optimization
Demonstrated ability to implement measurable optimization initiatives
Strong understanding of balancing performance improvements with reliability and stability requirements
Structured and data-driven approach to infrastructure decision-making
