Reliability Analytics Engineer

The Reliability Analytics Engineer is an onsite role based in Seattle, WA, focused on reliability data analytics, designing data pipelines, and building tools and dashboards to support reliability across Amazon Robotics fleets. The position integrates hands-on reliability engineering with a data-centric approach to scale evaluation across multiple product programs.

Responsibilities

• Define data requirements and specifications for reliability pipelines and collaborate with data engineering teams to design and validate ETL processes that ingest field failures, test data, and product telemetry from enterprise data lakes and EAM systems.
• Cleanse, validate, and prepare reliability datasets (including censored life data, field service records, and accelerated test results) to ensure accuracy before informing engineering decisions.
• Translate reliability questions into data queries and assessment workflows, converting ambiguous problems into repeatable, scalable evaluation methods that other engineers can reuse.
• Develop and maintain reliability assessment tools and automation using Python, AI/ML, and statistical libraries, including automated failure mode classification, survival calculators, and fleet health monitoring scripts.
• Design and specify dashboard requirements for reliability KPIs (availability, MTBF, MTTR, and failure rate trends); build prototypes and move them into production dashboards.
• Identify data gaps, define collection requirements for new failure modes or test programs, and collaborate with hardware test and field teams to close these gaps.
• Establish data governance practices for reliability datasets, including metadata standards, version control, and traceability to source systems to ensure reproducibility and auditability of findings.

Requirements

• BS degree in mechanical engineering or equivalent
• 3+ years of experience in mechanical engineering or an equivalent field
• Proficiency with data analysis tools such as Advanced Excel, SQL, Tableau, and Python
• Experience applying basic statistical methods (for example regression) to complex business problems

Technologies

• Python
• SQL
• Tableau
• Advanced Excel
• AWS S3
• AWS Redshift
• AWS EMR
• AWS RDS
• Reliasoft
• Minitab
• JMP
• AI/ML

Benefits

• Medical, Dental, and Vision Coverage
• Maternity and Parental Leave Options
• Paid Time Off (PTO)
• 401(k) Plan

A Day in the Life

• Perform Mean Cumulative Function and Weibull analyses on robot fleets to verify suspensions before distribution to the reliability team.
• Collaborate with data engineers to define telemetry pipeline requirements, including essential fields, censoring rules, and output schemas.
• Develop Python tools that automate reliability growth tracking from the data lake, apply models, and generate standard reports accessible to engineers.
• Provide failure rate data by subsystem to support DFMEA exercises.
• Refine machine learning classifiers that auto-tag field failure tickets by mode, improving accuracy with feedback from the sustaining team.
• Apply expertise in repairable systems, physics of failure, and life data to reliability assessments.
• Scale expertise through data tooling, automation, and AI to build systems that the entire team can operate.

About the Team

• Amazon Robotics Reliability Engineering brings together experts from diverse engineering disciplines to tackle complex optimization problems and enhance customer outcomes.
• The team operates in the face of ambiguity, maintains momentum, and supports the growth of every team member.