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ZoomInfoPosted 1 month ago

DevOps Engineer III

$113,400–$178,200 year

HybridWaltham, Massachusetts, United States

Full TimeSenior LevelLargeData Services

Job Summary

Design, provision, and manage data services on Kubernetes-based platforms including Amazon EKS and Google Kubernetes Engine. Implement infrastructure as code with Terraform and develop CI/CD pipelines using Jenkins, Argo CD, and GitHub Actions to enable automated testing and deployments. Deploy and support cloud-native data services such as Amazon Kinesis, AWS Glue, Google Pub/Sub, Dataflow, and BigQuery while leveraging AI-powered tooling to accelerate script generation and incident troubleshooting. Create automation scripts in Python and assist in establishing monitoring, logging, and alerting using Prometheus, Grafana, or Datadog. Participate in on-call rotations, incident triage, and post-incident reviews applying SRE best practices, and collaborate with engineers to ensure infrastructure aligns with application requirements. Document infrastructure, runbooks, and lessons learned to promote knowledge sharing across teams.

Required Qualifications

  • 4–6 years in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role
  • Production experience with AWS and/or GCP data services (e.g., Kinesis, Pub/Sub, Dataflow, BigQuery)
  • Hands-on experience managing containerized workloads on Kubernetes (EKS, GKE, or self-managed clusters)
  • Solid understanding of Terraform (or similar IaC tools) and Git-based workflows
  • Working knowledge of CI/CD platforms such as Jenkins, Argo CD, and/or GitHub Actions
  • Proficiency with Python or another scripting language for automation
  • Familiarity with observability stacks (CloudWatch, Datadog, etc.)
  • Fundamental grasp of SRE principles—service reliability, incident response, and performance monitoring
  • Effective communication skills and a collaborative mindset
  • Must be able to lift 50 lbs

Desired Qualifications

  • Demonstrated experience using AI-powered copilots, chat assistants, or AIOps platforms to accelerate infrastructure work or incident resolution
  • Experience with workflow orchestration tools (Apache Airflow, Cloud Composer)
  • Exposure to big-data frameworks (Spark, Flink) or modern data-lake architectures
  • Knowledge of cost-optimization techniques for cloud resources
  • Familiarity with event-driven architectures and message queues (Kafka, RabbitMQ)
  • Understanding of GitOps workflows and service mesh technologies such as Istio

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