Site Reliability Engineer

Pythian is hiring a Site Reliability Engineer to design, operate, and support reliable, observable, and cost-effective customer platforms across Linux, Kubernetes, and public cloud. The role focuses on platform reliability, automation and infrastructure as code, data platform operations, customer technical guidance, and supporting teammates.

Responsibilities:

• Improve reliability, availability, and visibility for customer platforms; plan/execute documented maintenance; drive remediation/automation
• Provide remote support for Linux-based infrastructure across data center and cloud environments
• Manage the full OS lifecycle, including installation, deployment (Kickstart, VMs, containers, bare metal), patching, and upgrades
• Design and implement automation for infrastructure deployment, configuration, and operations using scripting (Bash, Pythian) and tools (Ansible)
• Implement and evolve SLIs/SLOs, error budgets, capacity plans, and reliability reviews; harden observability (metrics/logs/traces) and keep alerts actionable
• Build and extend infrastructure as code (Terraform/CloudFormation), configuration management (Ansible/Puppet/Chef/Salt), and GitOps (Helm, Argo CD/Flux) pipelines
• Collaborate with support/app/data teams; reduce toil via scripting (Bash/Python) and small tools
• Data Platforms – pick one at depth, ramp on others:
• Elasticsearch/OpenSearch: cluster ops, shard/replica strategy, ILM/snapshots, performance/security, EFK integration
• Redis: cluster/Sentinel ops, failover drills, parameter tuning (maxmemory, timeouts), cache observability (hit ratio/latency/evictions), persistence/DR (RDB/AOF, snapshots)
• Kafka–Hadoop–Spark: topic/ACL management, retention/partitioning, connector basics, rolling upgrades; pipeline observability (lag/throughput/error rates); routine automation
• Facilitate technical discussions with customers; advise on platform roadmaps and architecture trade-offs
• Support and cross-train teammates; uphold documentation quality and internal knowledge sharing
• Participate in on-call, escalations, and change reviews; proactively reduce toil via scripting/automation

Requirements:

• Data Platforms expectation: bring depth in one area and the responsibility/willingness to ramp on the others in 60–90 days:
• Strong Linux administration/troubleshooting; Kubernetes fundamentals (workloads, networking, storage, upgrades, security, HA); one major cloud (GCP/AWS/Azure)
• IaC + config management experience; CI/CD/GitOps familiarity; scripting with Bash and/or Python
• Consulting mindset: clear English (written/spoken), estimation, stakeholder communication, documentation quality
• Elasticsearch/OpenSearch (ops, tuning, ILM/snapshots, security/EFK), or
• Redis (OSS/managed operations; data structures, eviction policies, RDB/AOF persistence)
• Kafka–Hadoop–Spark (production exposure; Confluent/MSK/Strimzi; Dataproc/EMR/CDP familiarity), or
• Security/IAM basics, budget/capacity awareness; Prometheus/Grafana experience; diagramming/runbook excellence