# Senior DevOps Engineer

**Company**: ZoomInfo
**Location**: Toronto, Ontario, Canada
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/zoominfo/jobs/8496473002
**Canonical**: https://yubhub.co/jobs/job_68a62835-66b

## Description

We are seeking a highly skilled and self-motivated Senior Embedded DevOps Engineer to support our engineering teams. This role will focus on driving changes and ensuring adherence to company-established standards for data infrastructure and CI/CD pipelines.

The ideal candidate will have strong experience working with AWS and/or GCP, cloud-based data streaming and processing services, containerized application deployments, infrastructure automation, and Site Reliability Engineering (SRE) best practices for performance and cost optimization.

Key Responsibilities:

- Drive initiatives to implement and enforce best practices for data streaming, processing, analytics and monitoring infrastructure.

- Deploy and manage services on Kubernetes-based platforms such as Amazon EKS and Google Kubernetes Engine (GKE).

- Provision and manage cloud infrastructure using Terraform, ensuring best practices in security, scalability, and cost-efficiency.

- Maintain and optimize CI/CD pipelines using Jenkins, ArgoCD, and GitHub Enterprise Actions to support automated deployments and testing.

- Work with cloud-native data services such as AWS Kinesis, AWS Glue, Google Dataflow, and Google Pub/Sub, BigQuery, BigTable

- Familiarity with workflow orchestration services such as Apache Airflow and Google Cloud Composer.

- Develop and maintain automation scripts and tooling using Python to support DevOps processes.

- Monitor system performance, troubleshoot issues, and implement proactive solutions to enhance reliability and efficiency.

- Implement SRE practices to improve service reliability, scalability, and cost-effectiveness.

- Analyze and optimize cloud costs, identifying areas for improvement and implementing cost-saving strategies.

- Ensure compliance with security policies and best practices in cloud environments.

- Drive adoption of company standards and influence data teams to align with best DevOps and SRE practices.

- Collaborate with cross-functional teams to improve development workflows and infrastructure.

Requirements:

- 7+ years of experience in a DevOps, Site Reliability Engineering, or Cloud Infrastructure role.

- Strong experience with AWS and GCP data services, including Kinesis, Glue, Pub/Sub, and Dataflow.

- Proficiency in deploying and managing workloads on Kubernetes (EKS/GKE) in production environments.

- Hands-on experience with Infrastructure-as-Code (IaC) using Terraform.

- Expertise in CI/CD pipeline management using Jenkins, ArgoCD, and GitHub Enterprise Actions.

- Programming skills in Python for automation and scripting.

- Experience with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or CloudWatch).

- Strong understanding of SRE principles, including performance monitoring, incident response, and reliability engineering.

- Experience with cost optimization strategies for cloud infrastructure.

- Self-motivated and driven, with a strong ability to influence and drive changes across multiple teams.

- Ability to work collaboratively in an agile environment and support multiple teams.

Preferred Qualifications:

- Experience with data lake architectures and big data processing frameworks (e.g., Apache Spark, Flink, Snowflake, BigQuery).

- Familiarity with event-driven architectures and message queues (e.g., Kafka, RabbitMQ).

- Experience with workflow orchestration tools such as Apache Airflow and Google Cloud Composer.

- Knowledge of service mesh technologies like Istio.

- Experience with GitOps workflows and Kubernetes-native tooling.

## Skills

### Required
- AWS
- GCP
- Kubernetes
- Terraform
- Jenkins
- ArgoCD
- GitHub Enterprise Actions
- Python
- Apache Airflow
- Google Cloud Composer
- CloudWatch
- Prometheus
- Grafana
- Datadog

### Nice to have
- Data lake architectures
- Big data processing frameworks
- Event-driven architectures
- Message queues
- Workflow orchestration tools
- Service mesh technologies
- GitOps workflows
- Kubernetes-native tooling