Amazon
Principal Customer Success Specialist, Observability & Security, AWS Specialist and Partner Organization
$183K–$284K
Seattle, WA +2 · Principal · Full-time · Posted Aug 31, 2026
- Location
- Seattle, WA
Mountain View, CA
San Francisco, CA - Experience
- 10+ years in technical, customer-facing roles; 5+ years directly advising enterprise customers.
About the job
Leads customer success strategy and advises major enterprise customers on adopting and realizing value from AWS observability and security solutions, including for AI and GenAI workloads.
What you'll do
- Own strategic enterprise customer engagements, advising executives and driving adoption, retention, expansion, and measurable business outcomes.
- Develop cross-domain adoption frameworks, playbooks, health metrics, and scaled enablement programs for observability and security.
- Coordinate with AWS product, sales, technical, and partner teams to address customer needs and influence service roadmaps.
What they ask for
- 10+ years in technical, customer-facing roles, with hands-on depth in observability, security, or both.
- 5+ years directly advising enterprise customers on cloud adoption, security posture, or observability strategy, with measurable outcomes.
- Experience designing and delivering enterprise-scale cloud adoption programs, including adoption plays, health scoring, and scaled enablement.
- Experience working cross-functionally and engaging CxO-level stakeholders to drive customer outcomes.
Nice to have
- AWS or similar cloud certification, or CISSP-ISSAP / CISSP-ISSEP.
- Experience spanning observability and security, including their convergence in enterprise environments.
Pay
210,200.00 - 284,300.00 USD annually (base salary); 210,200.00 - 284,300.00 USD annually (base salary); 182,800.00 - 247,300.00 USD annually (base salary)
Tools
AWS, AI, GenAI, ML, SIEM/SOAR, LLM, RAG
AI focus
Advises on observability and security for AI/ML and GenAI workloads, including AIOps, ML-powered anomaly detection, automated security remediation, threat modeling, model inference monitoring, prompt pipelines, RAG architectures, and AI data governance.
Model-written summary · the original posting is authoritative