Datacenter Reliability Engineer, Infrastructure Quality

Amazon Web Services (AWS); Herndon (VA); On-site; USD 117,000 - 160,000; Full time

Job summary

Amazon Data Services, Inc. in Herndon, VA is seeking an Infrastructure Reliability Engineer to drive reliability risk identification and mitigation for datacenter infrastructure.

You will perform root cause analyses of critical failures and collaborate with suppliers to improve availability and security across the AWS global footprint. The role requires expertise in physics-of-failure approaches, lifecycle and stress analysis, and statistical modeling to quantify risk and guide improvements.

Qualifications

• Bachelor’s or Master’s degree in Reliability Engineering, Physics, Mechanical or Materials Engineering or related field.
• 4+ years of Reliability Engineering work experience in high reliability industry.
• Experience with accelerated life testing, stress analysis and finite element analysis.

Responsibilities

• Identify and mitigate reliability risks for datacenter infrastructure and security equipment.
• Drive root cause analysis of critical equipment failures and corrective actions.
• Lead vendor auditing and quarterly review processes to improve availability.
• Develop datacenter system reliability models and perform risk analysis.
• Collaborate with internal and external partners to meet requirements.

Skills

Root cause analysis

Problem solving

Communication skills

Reliability engineering

Thermal analysis

Team collaboration

Education

Bachelor's or Master’s degree in Reliability Engineering or related field

Tools

Statistical modeling tools

Job description

Amazon Data Services, Inc. in Herndon, VA is seeking an Infrastructure Reliability Engineer to drive reliability risk identification and mitigation for datacenter infrastructure.

You will perform root cause analyses of critical failures and collaborate with suppliers to improve availability and security across the AWS global footprint. The role requires expertise in physics-of-failure approaches, lifecycle and stress analysis, and statistical modeling to quantify risk and guide improvements.