쿠팡에서 데이터 센터 관측성 및 SRE 플랫폼을 설계하고 운영할 Staff 엔지니어를 채용합니다. Go 또는 Python에 능숙하며 Kubernetes 내부 구조에 대한 깊은 이해와 대규모 분산 시스템 운영 경험이 필수입니다. 미션 크리티컬한 시스템의 신뢰성을 책임질 역량 있는 분들의 많은 지원 바랍니다.
We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion-dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce.
We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day.
Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional trade-offs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world.
As a Staff Data Centre Observability and Site Reliability Engineer, you will own the design and operation of scalable observability platforms to ensure the reliability, performance, and availability of datacentre services. You will apply SRE best practices, automation, and performance optimization to deliver resilient infrastructure. This role partners closely with engineering teams and vendors to drive operational excellence while maintaining security and compliance standards.
• Design, implement, and maintain observability solutions for datacentre infrastructure.
• Develop, deploy, and maintain the operational and reliability components of a large-scale Observability and Telemetry collection platform, emphasizing performance at scale, real-time monitoring, logging, and alerting.
• Participate in and enhance the entire lifecycle of services, from inception and design to deployment, operation, and refinement.
• Develop and optimize monitoring systems to ensure high availability and performance.
• Create and manage dashboards, alerts, and reports to provide visibility into system health and performance.
• Implement SRE best practices to improve the reliability, scalability, and performance of datacentre services.
• Develop and maintain automation scripts for infrastructure provisioning, monitoring, and management.
• Conduct root cause analysis and post-mortem reviews to prevent recurrence of incidents.
• Analyze and optimize the performance of datacentre systems and applications.
• Implement best practices for resource utilization and efficiency. Collaboration:
• Work closely with other engineering teams to understand and meet their observability and reliability requirements.
• Collaborate with hardware and software vendors to evaluate and integrate new technologies.
• Ensure that observability and reliability solutions comply with security policies and industry standards.
• Implement and maintain security measures to protect data and infrastructure.
• Provide support for observability and reliability-related issues, including debugging and resolving hardware and software problems.
• Develop and maintain documentation for troubleshooting procedures and best practices.
• Stay updated with the latest advancements in observability and SRE technologies and integrate them into the infrastructure.
• Continuously improve the reliability, scalability, and performance of datacentre services.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
Experience: 8–12 years of progressive software engineering experience, with a heavy emphasis on distributed systems, cloud-native architectures, or platform operations.
Programming: Strong proficiency in Go or Python, with a deep understanding of networked systems and performance optimization.
Orchestration: Expert-level knowledge of Kubernetes internals (scheduling, controllers) and containerization ecosystems.
Traffic Management: Proven experience with load balancing, service mesh, and request routing at scale.
Operational Excellence: A strong "ownership" mindset with a track record of maintaining mission-critical, high-availability systems in production.
AI/ML Domain Knowledge: Prior experience building infrastructure specifically for LLM inference or large-scale training clusters.
Low-Level Optimization: Familiarity with inference, including mixed precision, kernel tuning, or custom hardware accelerators.
Public/Private Cloud: Experience managing hybrid-cloud or multi-AZ deployments across AWS, Azure, or GCP.
Compliance: Experience operating in regulated environments with strict security and compliance requirements.
