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Pythian

Cloud migration and managed analytics services

Site Reliability Engineer

Site Reliability EngineeringPosted on Nov 12
Full-timeRemote in USA$80k - $90k /yr

About the Job

Requirements

  • Strong Linux administration and troubleshooting skills.
  • Kubernetes fundamentals, including workloads, networking, storage, upgrades, security, and high availability.
  • Experience with one major cloud platform: Google Cloud Platform, Amazon Web Services, or Microsoft Azure.
  • Experience with infrastructure as code and configuration management.
  • Familiarity with continuous integration/continuous delivery and GitOps.
  • Scripting skills with Bash and/or Python.
  • Clear written and spoken English, estimation, stakeholder communication, and documentation skills.
  • Depth in at least one data platform area: Elasticsearch/OpenSearch operations, tuning, ILM/snapshots, security/EFK; Redis operations, data structures, eviction policies, and RDB/AOF persistence; or production exposure to Kafka, Hadoop, and Spark and familiarity with Confluent/MSK/Strimzi or Dataproc/EMR/CDP.
  • Ability and willingness to ramp up on other data platform areas within 60–90 days.
  • Ability to fulfill the requirements necessary to obtain a background check.

Responsibilities

  • Improve reliability, availability, and visibility for customer platforms; plan and execute documented maintenance; and drive remediation and automation.
  • Provide remote support for Linux-based infrastructure across data center and cloud environments.
  • Manage the full operating-system lifecycle, including installation and deployment on virtual machines, containers, and bare metal, as well as patching and upgrades.
  • Design and implement infrastructure deployment, configuration, and operations automation using scripting and tools such as Ansible.
  • Implement and evolve service-level indicators, service-level objectives, error budgets, capacity plans, and reliability reviews; improve observability across metrics, logs, and traces; and keep alerts actionable.
  • Build and extend infrastructure-as-code, configuration-management, and GitOps pipelines using tools such as Terraform, CloudFormation, Ansible, Puppet, Chef, Salt, Helm, Argo CD, and Flux.
  • Collaborate with support, application, and data teams, reducing toil through scripting and small tools.
  • Operate Elasticsearch/OpenSearch clusters, manage shard and replica strategy, ILM and snapshots, performance and security, and EFK integration.
  • Operate Redis clusters and Sentinel, conduct failover drills, tune parameters such as maxmemory and timeouts, monitor cache hit ratio, latency, and evictions, and manage persistence and disaster recovery using RDB/AOF and snapshots.
  • Manage Kafka topics and access-control lists, retention and partitioning, connector basics, rolling upgrades, pipeline observability, and routine automation across Kafka/Hadoop/Spark systems.
  • Facilitate technical discussions with customers and advise on platform roadmaps and architecture trade-offs.
  • Support and cross-train teammates, maintain documentation quality, and share internal knowledge.
  • Participate in on-call coverage, escalations, and change reviews, and proactively reduce toil through scripting and automation.

Desired Qualifications

  • Security and identity/access-management basics.
  • Budget and capacity awareness.
  • Prometheus and Grafana experience.
  • Diagramming and runbook skills.

About the Company

Pythian

Cloud migration and managed analytics services

Pythian helps organizations move, manage, and analyze data and IT infrastructure. It provides cloud migration, managed services, and advanced analytics to help businesses use cloud platforms such as Google Cloud, AWS, and Azure. Its solutions include moving data and applications to the cloud, running and monitoring those systems, and turning data into insights through machine learning, artificial intelligence, and data science. Pythian differentiates itself by offering a fully managed analytics platform that unifies a company’s data across sources and by combining expert consulting with ongoing operations to ensure data systems run smoothly. The company’s goal is to enable enterprises to improve decision-making, reduce IT friction, and drive growth by leveraging cloud technology and data analytics.

Company size
501-1,000
Company stage
Early VC
Total funding
$21M
Headquarters
Ottawa, Canada
Founded
1997

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