DevOps Engineer III

What You’ll do:

• 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.

What you bring:

• 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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