Senior Kubernetes Engineer

Overview We are seeking an expert Kubernetes engineer to configure, deploy, and operate mission-critical Amazon EKS infrastructure supporting petabyte-scale data processing workloads. This role requires deep technical expertise in Kubernetes internals, large-scale cluster management, and Apache Spark optimization to support thousands of concurrent jobs processing large volumes of Bigdata.

Core Responsibilities

• Design, deploy, and maintain production-grade Amazon EKS clusters architected for petabyte-scale data processing with high availability and fault tolerance
• Operate in air-gapped private VPC environments without internet access, managing secure package repositories and container registries
• Debug and resolve complex distributed systems issues across EKS, Karpenter, and Spark, including scheduling bottlenecks, node scaling delays, and cascading failures under heavy load
• Implement Karpenter consolidation and disruption policies balancing cost optimization with job resiliency
• Manage spot and on-demand instance strategies with robust node interruption handling
• Define and enforce ResourceQuotas, LimitRanges, and PriorityClasses to ensure fair resource distribution and prevent resource starvation
• Configure persistent volume claims and provision container-native storage using Amazon EBS CSI driver for block storage and Amazon EFS CSI driver for shared file access
• Optimize storage configurations for resiliency, high-throughput data processing workloads
• Implement logging, alerting, and anomaly detection to identify provisioning failures, executor loss, and throughput degradation before broader system impact
• Design fault-tolerant architectures with retry strategies, checkpointing, and graceful degradation patterns minimizing re-computation on failure
• Develop and manage configs in a private VPC without internet access

Preferred Qualifications

• Kubernetes certification (CKA, CKAD, or CKS) & AWS certifications
• Active contributions to open-source Kubernetes, Karpenter, or Spark projects
• Familiarity with FinOps practices and cost optimization at scale
• Background in data engineering or analytics platforms