Senior Kubernetes Engineer for Petabyte-Scale Data Ops

Strategio Inc.; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Strategio in the DMV/NY area seeks a Senior Kubernetes Engineer to design, deploy, and operate production-grade Amazon EKS clusters within air-gapped/private environments handling petabyte-scale workloads.

You will architect highly available Kubernetes environments, manage Karpenter-based scaling, and implement robust storage and monitoring to support thousands of concurrent jobs. The role is hybrid, requires deep expertise in Spark workloads.

Qualifications

• Extensive hands-on experience designing and operating production Kubernetes environments.
• Deep expertise with Amazon EKS and AWS cloud infrastructure.
• Strong ability to troubleshoot complex performance and reliability issues in large clusters.

Responsibilities

• Design, deploy, and operate production-grade Amazon EKS clusters.
• Architect highly available and fault-tolerant Kubernetes environments for thousands of concurrent jobs.
• Operate infrastructure within air-gapped private VPC environments.
• Investigate and resolve distributed systems issues across EKS, Karpenter, and Spark.
• Troubleshoot scheduling bottlenecks and node scaling delays under heavy workloads.
• Define and enforce resource quotas, limits, and priority classes.
• Configure persistent volumes with EBS and EFS CSI drivers.
• Implement logging, monitoring, and anomaly detection to prevent failures.

Skills

Kubernetes

Amazon EKS

Karpenter

Apache Spark

Distributed systems

Troubleshooting

Resource management

Air-gapped environments

Cloud infrastructure

Spot/On-Demand strategies

Tools

Kubernetes storage

EKS tooling

AWS CLI

Job description

Strategio in the DMV/NY area seeks a Senior Kubernetes Engineer to design, deploy, and operate production-grade Amazon EKS clusters within air-gapped/private environments handling petabyte-scale workloads.

You will architect highly available Kubernetes environments, manage Karpenter-based scaling, and implement robust storage and monitoring to support thousands of concurrent jobs. The role is hybrid, requires deep expertise in Spark workloads.