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Amazon · 2 weeks ago

Sr. Manufacturing Quality Engineer - OISL, Amazon Leo

Redmond, Washington, USA

Full-time

Onsite

Mid, Senior Level

$129K/yr - $214K/yr

4+ years exp

Amazon is a leading company focused on delivering fast, reliable internet connectivity through its low Earth orbit satellite network, Amazon Leo. The Sr. Manufacturing Quality Engineer will lead quality management activities, drive process improvements, and ensure compliance with quality standards in the development and production of satellite payloads.

Artificial Intelligence (AI)DeliveryE-CommerceFoundational AIRetail

No H1B

U.S. Citizen Only

Responsibilities

Optical Inter-Satellite Link, work on new design and development team for upcoming design change. Drive the APQP tool such as DFMEA, PFMEA, Control Plan and Process flow

Lead the complex Optical assembly test failure investigation by engaging the design and manufacturing team

Identified the critical optical test parameter and utilized the SPC tool to work proactively to understand the product and process variations

Develop and lead Amazon Leo Quality management activities, including process definition and validation for assembly and test methods, part and process qualification, receiving inspection plans, in-process inspection capabilities, and process monitoring capabilities

Lead Material Review Board discussions in Amazon's Finished Satellite Payload Processing facility, determining product dispositions for nonconforming material

Lead the development and launch of multiple aspects of the Quality Management System across Amazon Leo

Dive deep into root cause corrective action activities for the production processes immediately before Amazon Leo satellites are loaded in the launch vehicle

Work directly with Receiving Inspection, Supplier Quality, and Engineering teams to develop and qualify inspection, monitoring, and verification methods for the Amazon Leo Production System

Be a subject matter expert in launch vehicle payload processing quality assurance for the Amazon Leo Production System

Perform daily defect resolution as the Quality member

Investigates Production System issues to reduce defect rates and drive root cause corrective actions

Implements the Amazon Leo Quality System through process development and launch mechanisms

Partners with cross-functional groups to develop and qualify internal production processes, including Quality Assurance processes

Conduct rigorous Continuous Improvement activities to prevent recurrence of non-conformances during assembly

Perform audit activities to ensure compliance to QMS requirements

Develop and modify process flow diagrams, PFMEAs, control plans, MSAs, and other APQP artifacts to support continued high quality production

Working within an AS9100 (or similar) certified quality system

Qualification

APQPRoot Cause AnalysisQuality Management SystemMechanical EngineeringElectrical EngineeringData Querying LanguagesStatistical SoftwareInterdisciplinary TeamworkContinuous ImprovementProject Management

Required

Experience in developing functional specifications, design verification plans and functional test procedures

Bachelor's degree in Mechanical or Electrical Engineering or a related field

4+ years of experience in developing functional specifications, design verification plans and functional test procedure

4+ year experience in leading the containment activities

4+ year experience in leading the APQP (PFMEA, CP, PFD, SPC, MSA)

Preferred

Master's degree in electrical engineering, computer engineering, or equivalent

Experience working with interdisciplinary teams to execute product design from concept to production

Experience with the project management of technical projects

4+ years of experience in performing RCA for functional test failure for PCBA or mechanical failures

4+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

4+ years experience in semi-automated and fully automated process improvement to add error proofing to system such as Kyence, Cognex or other AI/ML Vision system