Job Description

What You Will Do

• Collaborate with senior SREs to enhance incident response, reinforce critical data paths, and facilitate scalable, cost-efficient operations in support of PDO’s objectives for operational excellence and AI/cloud/data platform adoption.

• Support the maintenance and enhancement of production environment for data platform services to ensure high availability and performance for mission-critical workloads.

• Manage and optimize big data and streaming platforms like Kafka, Hadoop, Spark, and Hive on AWS, focusing on configuration, tuning, and daily operations.

• Assist in defining SLOs, error budgets, capacity plans, and scaling strategies for data platform components to support new AI and data products.

• Participate in on-call rotations to maintain high availability of services, leveraging tools like PagerDuty to triage alerts and provide technical responses to incidents impacting data and analytics platforms.

• Resolve production issues through cross-team collaboration and adherence to established incident management practices.

• Contribute to blameless post-incident reviews to develop reliability improvements, runbooks, and automation.

• Build and improve automation and tooling using Go, Python, or scripting to standardize deployment and troubleshooting for data services.

• Support CI/CD pipelines using TeamCity or Github Actions to enable safe and frequent service deployments.

• Utilize Infrastructure as Code, including Terraform and AWS CloudFormation, to maintain repeatable cloud infrastructure.

• Operate Kubernetes-based environments on AWS EKS, managing the lifecycle and scaling of containerized data services.

• Implement progressive delivery strategies, such as blue/green and canary deployments, to minimize release risks.

What You Need to Succeed

Experience and Education

• 4–6 years of relevant industry experience in Site Reliability Engineering, DevOps, Production Engineering, or similar roles supporting large‑scale distributed systems and/or data platforms.

• BS/MS in Computer Science, Computer Engineering, Mathematics, or a related technical field, or equivalent practical experience.

Technical Skills

• Experience deploying and operating services on AWS or Azure, including on-call support.

• Expertise with data and streaming platforms like Kafka, Hadoop, Spark, or Hive.

• Proficiency in Go, Python, or Bash for automation; Java/Spring Boot knowledge is beneficial.

• Skill in building tools and utilities using REST APIs or gRPC.

• Proficiency with CI/CD tools such as TeamCity, GitHub Actions, or Jenkins.

• Experience with Infrastructure as Code (Terraform or CloudFormation). Familiarity with Kubevela/Crossplane is a plus

• Working knowledge of Kubernetes (EKS) and Docker for deployment and resource management.

• Familiarity with AWS services like RDS, EMR, Redshift, MSK, and ECS.

• Experience with observability and logging tools like Datadog or ELK.

• Understanding of distributed systems, networking, storage, and operating systems.

• Familiarity with agile methodologies like Scrum and Kanban.

• Ability to solve infrastructure problems using software engineering and participate in incident response.

• Effective collaboration skills aligned with business priorities like AI and cloud adoption.

Bonus Points

• Kubernetes/AWS certifications

• Contributions to open source projects

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