AWS Cloud Performance & Reliability Engineer (6–9 Years)

At Quest Global, it’s not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.

We are seeking an AWS Cloud Performance & Reliability Engineer with strong experience in performance engineering, scalability testing and capacity planning for cloud-native and microservices-based applications. The role will focus on assessing application and infrastructure performance in AWS environments, identifying bottlenecks, optimising resource utilisation and ensuring that workloads meet defined performance, availability, scalability and cost objectives.

The ideal candidate combines hands-on performance testing expertise with practical knowledge of AWS, Kubernetes, observability and distributed systems. They should be comfortable analysing application and infrastructure metrics, validating scaling strategies and translating test results into actionable reliability and capacity recommendations.

Key Responsibilities

·       Design, develop and execute performance, load, stress and scalability tests for distributed applications, microservices and cloud-native workloads deployed on AWS.

·       Develop representative workload models and performance test scenarios to evaluate application behavior under normal, peak and stress conditions.

·       Analyze application and infrastructure performance metrics to identify bottlenecks, latency issues, throughput limitations and resource constraints.

·       Perform capacity planning and recommend appropriate compute, container and infrastructure configurations based on workload characteristics and performance objectives.

·       Assess and optimize containerized workloads running on Amazon EKS or Amazon ECS, including pod sizing, replica configuration and resource requests and limits.

·       Evaluate horizontal and vertical scaling approaches and define appropriate autoscaling strategies using Kubernetes HPA, KEDA or other relevant mechanisms.

·        Determine optimal infrastructure configurations, including CPU, memory and replica counts, ensuring high availability and performance under defined workload conditions

·       Use AWS CloudWatch, Prometheus, Grafana and other observability tools to monitor application health, infrastructure utilization, performance trends and reliability indicators.

·       Analyze AWS compute, storage, networking and managed-service utilization to identify optimization opportunities and improve operational and cost efficiency.

·       Conduct performance analysis across application, middleware, database, messaging and infrastructure layers to isolate root causes of performance issues.

·       Assess system availability, resilience and recovery characteristics, and recommend improvements to meet reliability and service-level objectives.

·       Work with messaging and streaming platforms, including Apache Kafka and Amazon MSK, to understand throughput, consumer processing and performance characteristics.

·       Establish performance baselines, service-level indicators (SLIs), service-level objectives (SLOs), utilisation thresholds and capacity expansion criteria.

·       Collaborate with software engineering, architecture, DevOps and cloud infrastructure teams to implement performance and reliability improvements.

• ·       Prepare performance test reports, capacity assessments, infrastructure sizing recommendations and technical documentation.
• ·       Adhere to established procedures, guidelines, and policies.
• ·       Maintain customer focus, integrity, and collaboration in the workplace.
• ·       Contribute openly and transparently to internal efforts for ensuring a strong workplace culture.

We are known for our extraordinary people who make the impossible possible every day. Questians are driven by hunger, humility, and aspiration. We believe that our company culture is the key to our ability to make a true difference in every industry we reach. Our teams regularly invest time and dedicated effort into internal culture work, ensuring that all voices are heard.

We wholeheartedly believe in the diversity of thought that comes with fostering a culture rooted in respect, where everyone belongs, is valued, and feels inspired to share their ideas. We know embracing our unique differences makes us better, and that solving the worlds hardest engineering problems requires diverse ideas, perspectives, and backgrounds. We shine the brightest when we tap into the many dimensions that thrive across over 21,000 difference-makers in our workplace.

Skills and Qualifications

·        Strong experience in performance engineering, reliability engineering, scalability testing or capacity planning for distributed and cloud-native applications.

·        Hands-on experience with performance testing tools such as Apache JMeter, LoadRunner, Gatling or equivalent.

·        Practical experience with AWS services and cloud infrastructure, particularly Amazon EKS, EC2, ECS and CloudWatch.

·        Strong understanding of Kubernetes, Docker, container orchestration, pod resource requests and limits, HPA and cluster autoscaling concepts.

·        Experience with monitoring and observability tools such as Prometheus and Grafana, including the ability to interpret application and infrastructure metrics.

·        Good understanding of microservices architecture, REST APIs, distributed systems and service-to-service communication. Ability to analyze application source code and understand microservices interactions and API dependencies.

• ·        Hands-on experience with Kubernetes CPU/memory resource tuning and pod sizing.
• ·        Experience configuring and validating autoscaling using Kubernetes HPA and KEDA.
• ·        Experience with Prometheus metrics and defining custom application-level scaling triggers
• ·        Experience designing JMeter scripts for individual microservices and analyzing performance test results

·        Knowledge of AWS networking fundamentals, load balancing and application traffic management, including VPC, security groups, ALB and NLB.

·        Understanding of AWS capacity planning, infrastructure sizing, resource utilization and cloud cost optimization; familiarity with AWS Compute Optimizer and Cost Explorer is an advantage.

·        Experience with messaging and streaming platforms such as Apache Kafka or Amazon MSK is desirable.

·        Familiarity with Linux, networking fundamentals, application profiling and JVM performance analysis is desirable.

·        Strong analytical, troubleshooting and problem-solving skills, with the ability to convert performance data into actionable recommendations.

·        Good written and verbal communication skills, with the ability to collaborate across engineering, architecture, DevOps and infrastructure teams.

• Mandatory Skills
• ·        Apache JMeter – hands-on performance and load testing.
• ·        AWS – practical experience with EKS and cloud infrastructure.
• ·        Kubernetes – CPU/memory tuning, pod sizing and autoscaling.
• ·        Prometheus/Grafana – monitoring and metrics-based scaling.
• ·        Microservices – performance analysis and component-level testing