ZoomInfo Technologies
Job Summary
ZoomInfo’s Infrastructure Engineering organization is looking for a results-oriented DevOps Engineer III to strengthen our data platform teams. You will help build and maintain cloud-native data streaming, processing, and analytics infrastructure in AWS and GCP, working closely with Senior and Staff DevOps Engineers to deliver reliable, scalable systems that power our industry-leading GTM intelligence products.
Key Responsibilities
Contribute to the design, provisioning, and management of data services on Kubernetes-based platforms (Amazon EKS & Google Kubernetes Engine).
Implement infrastructure as code with Terraform, ensuring security, scalability, and cost awareness.
Develop and maintain CI / CD pipelines (Jenkins, Argo CD, 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.
Leverage AI-powered tooling (e.g., GitHub Copilot, generative-AI chat / ops assistants, and AIOps platforms) to accelerate script generation, configuration validation, and incident troubleshooting.
Create automation scripts and internal tooling in Python to streamline DevOps workflows.
Assist in establishing monitoring, logging, and alerting using Prometheus, Grafana, CloudWatch, or Datadog; incorporate AI-driven anomaly detection where applicable.
Participate in on-call rotations, incident triage, and post-incident reviews; apply SRE best practice.
Collaborate with engineers and software developers to ensure infrastructure aligns with application requirements and company standards.
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.
Preferred 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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Key Responsibilities
Contribute to the design, provisioning, and management of data services on Kubernetes-based platforms (Amazon EKS & Google Kubernetes Engine).
Implement infrastructure as code with Terraform, ensuring security, scalability, and cost awareness.
Develop and maintain CI / CD pipelines (Jenkins, Argo CD, 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.
Leverage AI-powered tooling (e.g., GitHub Copilot, generative-AI chat / ops assistants, and AIOps platforms) to accelerate script generation, configuration validation, and incident troubleshooting.
Create automation scripts and internal tooling in Python to streamline DevOps workflows.
Assist in establishing monitoring, logging, and alerting using Prometheus, Grafana, CloudWatch, or Datadog; incorporate AI-driven anomaly detection where applicable.
Participate in on-call rotations, incident triage, and post-incident reviews; apply SRE best practice.
Collaborate with engineers and software developers to ensure infrastructure aligns with application requirements and company standards.
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.
Preferred 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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