what you will do?
- work with large-scale data engineering infrastructures and data native technologies such as Spark/EMR, Flink, Apache Pinot, Kafka, Airflow, Tableau, NiFi, Metabase, and Databricks
- work with Observability tools like Loki, Victoriametrics and Datadog
- showcase understanding of best practices in running and managing self managed platforms on kubernetes, ensuring complete observability, HA, and self-served CI/CD system
- foster cross-team collaboration, building and maintaining relationships with customer teams, architects, and engineering teams to jointly achieve key deliverables ensuring production scalability and stability
- demonstrate strong troubleshooting and debugging skills, including conducting post-incident reviews, root cause analysis, and triaging product or system issues to analyze sources, impacts, and resolve them for service operations and quality
you should apply if you:
- have experience in SRE/DevOps, with a focus on distributed cloud native systems design, observability, container orchestration, maintenance, and troubleshooting
- experience with public cloud platforms, preferably AWS
- have hands-on experience in Kubernetes/EKS, building and operating large-scale production systems with stringent SLOs & SLAs
- are proficient in modern DevOps programming and scripting languages: Shell, Python, GoLang
- demonstrate experience in Linux Infrastructure management and systems administration with Linux
- have experience with Infrastructure as code & Configuration management using tools like Terraform, Helm, Ansible
- have expertise in Continuous Integration and Deployment (CI/CD) and release orchestration using Jenkins, ArgoCD, GitHub Actions, etc.
- have expertise in big data systems like Spark/EMR, Flink, Airflow etc.
- have expertise in pubsub solutions like Kafka.
- are familiar with system observability tools such as ELK/EFK, Prometheus, Grafana, alert manager, Sysdig, Datadog, Victoria Metrics, etc.
- have exceptional interpersonal, verbal, and written communication skills
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