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GCP Devops SRE Lead

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Onsite

GCP Devops SRE Lead

TechClub Inc

GCP Devops SRE Lead

Skills

Google Cloud PlatformReactNodePythonJavaGrafanaSplunkAIElasticsearchPrometheusKubernetesApache Airflow

5 days ago

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Important information


Contract type:

Freelance / Contract

Location:

Woonsocket, RI 02895, USA

Work mode:

Onsite

Published on:

22 October 2026

What they need


GCP Devops SRE Lead

Woonsocket, RI - hybrid

W2 contract

Job Description/ Responsibilities

  • 8+ years of Senior Software engineering experience in SRE, DevOps, platform engineering, or related production-systems roles in distributed systems at production scale with active on-call responsibility

  • Demonstrated experience as an on-call Incident Commander (IC) for P1 or P2 incidents — structured leadership updates, not just participant involvement

  • Experience tuning and validating time-series anomaly detection models in a production observability context — this is a Required qualification, not a preferred one; anomaly-based detection is a core function of this role

  • Strong programming proficiency in Python, React, and Java at production quality — capable of writing operational tooling that other engineers will rely on

  • Hands-on experience designing SLIs, SLOs, and managing error budgets for customer-facing or business-critical services

  • Deep observability platform experience: Prometheus, Grafana, OpenTelemetry, and at least two of the log aggregation solution (Loki, Splunk, Elasticsearch)

  • Fleet-scale deployment awareness: familiarity with progressive rollout strategies, blast radius management, and configuration drift as a reliability risk in large unattended node deployments

  • Strong cloud platform expertise in Google Cloud Platform (GCP) and Rancher K3s.

  • Advanced Kubernetes operational experience: debugging, resource management, networking policies, and workload failure modes. Experience with AI-assisted tooling and development.

  • Experience diagnosing and resolving workflow orchestration issues, batch processing failures, scheduler performance problems, and building observability on data pipeline : Apache Airflow and Tidal.

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