Competitive Service Jobs in Remote
372,305 open positions · Updated daily
Looking for Competitive Service jobs in Remote? Browse our curated listings with transparent salary information to find the perfect Competitive Service position in the Remote area.
Senior Account Executive - Enterprise Division
Company: GANNETT
Location: Remote
Posted Feb 07, 2025
Senior Employee & Labour Relations Manager
Company: ServiceNow
Location: Dublin, Ireland / Remote
Posted Feb 07, 2025
Frequently Asked Questions
What are typical salary ranges by seniority for Competitive Service roles?
Junior Cloud Engineers earn $80,000–$110,000, mid‑level SREs range $110,000–$150,000, senior architects and lead incident responders command $150,000–$220,000+, with executive roles exceeding $250,000 depending on company size and region.
What skills and certifications are required in Competitive Service?
Core skills include Kubernetes, Terraform, CI/CD (GitLab, Spinnaker), observability (Prometheus, Grafana), and incident‑response platforms (PagerDuty, Opsgenie). Certifications such as AWS Certified Solutions Architect, Certified Kubernetes Administrator (CKA), Certified DevSecOps Professional, and ITIL Foundation are highly valued.
Is remote work available for Competitive Service positions?
Yes, most Competitive Service roles offer 100% remote or hybrid arrangements. Companies prioritize cloud‑first toolsets, which enable distributed teams to manage services from anywhere while maintaining strict security controls.
What career progression paths exist in Competitive Service?
Typical progression starts with Support Engineer → SRE → Cloud Architect → Incident Response Lead → Technical Program Manager or VP of Reliability. Continuous learning and cross‑functional experience accelerate movement into leadership and strategy roles.
What industry trends are shaping Competitive Service today?
Emerging trends include multi‑cloud orchestration, IaC automation, AI‑Ops for predictive incident mitigation, zero‑trust security architecture, and the integration of observability with machine‑learning analytics to reduce mean time to recovery.
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