Job summary
Our AWS Infrastructure Reliability & Quality (IRQ) engineering team provides engineering support for our data center infrastructure equipment (Air Handling Unit, Switchgear, Breaker, Panel Board, UPS, Transformer, Generator, ATS etc.). As a member of this team you will be proactively driving quality and reliability risk identification, assessment and mitigation for data center equipment. You will also be responsible for root cause analysis of critical equipment failures, supplier process breakdown and drive continuous improvements to improve datacenter availability for AWS customers. You will work closely with both internal and external partners including suppliers to define product specifications, risk identification plans and mitigations. Internally you will collaborate with AWS Engineering, Procurement, Construction, Commissioning, Operations and Field Engineering teams. Externally you will manage supplier qualification, quality and reliability monitoring, supplier issue resolution and supplier development and continuous improvement initiatives that span the product lifecycle. You must have can-do attitude, be ownership minded, independent, action- and results-oriented to succeed in our open collaborative environment.

Key job responsibilities

• Develop, implement and maintain equipment quality and reliability roadmaps by collaborating with engineering, operations, and procurement teams.
• Define, monitor and achieve the correct quality/reliability performance targets for each equipment.
• Verify AWS quality standards are met at suppliers through in-person and remote audits.
• Establish and monitor end-of-line and incoming inspection/first article inspection plans.
• Support supplier and equipment qualification and assessment processes in support of procurement teams including issue resolution.
• Collaborate globally with suppliers to resolve field issues through Root Cause Analysis and corrective actions. Escalate complex failure investigations to AWS Senior/Principal Engineers.
• Develop and support suppliers with product improvement initiatives and Key Performance Indicators (KPI). Provide a feedback mechanism from suppliers to internal teams to resolve joint quality issues.
• Support internal AWS teams in New Product Development (NPD) initiatives including Failure Mode and Effect Analysis (FMEA) of design and manufacturing processes. Ensure AWS products meet or exceed industry standards for initial quality and long-term reliability performance.
• Analyze product design assumptions and AWS operational requirements to identify and mitigate equipment performance risks.
• Drive Continuous Process Improvement strategy through identification of new qualification criteria, test requirements, preventative maintenance checkpoints or specification to improve overall equipment resilience
• Successfully handle concurrent projects, sometimes in multiple geographical regions.
• Travel required, both international and domestic, approximately 30-50%

• MS or PhD in Electrical, Mechanical, or Manufacturing Engineering or similar related field. • 5+ years of work experience in quality/reliability risk identification and assessment from component to system level applying analytical, experimental and statistical approaches to evaluate product design and manufacturing quality/reliability levels. • Experience with managing proactive, effective, and frugal quality/reliability strategies throughout product design, manufacture and deployment stages. • Experience with data center operations and infrastructure equipment (Air Handling Unit, Switchgear, Breaker, Panel Board, UPS, Transformer, Generator, ATS etc.). • Experience with modern manufacturing processes, ISO-9000, quality control plans, and problem-solving methodologies. • Experience with accelerated life testing, stress analysis and finite element analysis. • Proficiency in the development of data, dashboards, and reports, including data cleansing and analysis. • Experience in data analysis using Tableau, Minitab, R or Python will be an added advantage • Experience with big data analytics • Excellent verbal and written communication skills.