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The Sr Staff Metrology & Measurement Engineer will architect a metrology-driven inspection and data analytics function for hyperscale server and rack integration. The Sr Staff Metrology & Measurement Engineer assist in design and operationalize a greenfield IQC lab, establish advanced measurement systems and analytics frameworks, from liquid cooling hardware to high-speed interconnects against print specifications and CTQ requirements before release to production.
Job Responsibility:
Contribute to transformation initiatives, helping set new standards for how ZT approaches manufacturing risk analysis, quality, and continuous improvement
Define and implement new systems, processes, or frameworks that support the smart factory vision, including automation, metrology, advanced inspection and testing, and predictive analytics
Leverage deep, data-rich environments and tools to generate insights that improve yield, reliability, and throughput
Collaborate across design, quality, manufacturing, test, and supplier engineering to drive solutions that integrate seamlessly into production
Apply advanced statistical and analytical methods to identify, quantify, and control risk in complex manufacturing environments
Translate DFM and PFMEA outputs into actionable process controls, ensuring proactive prevention of variation rather than reactive correction
Define and implement the Incoming Quality Lab framework, including inspection methods, equipment strategy, calibration systems, and SOPs
Select, qualify, and maintain advanced metrology and test systems (CMM/OMM, cable and signal integrity analyzers etc)
Manage calibration programs and maintain compliance with ISO and customer quality standards
Establish and enforce inspection standards and CTQs directly tied to system performance and reliability
Deploy advanced sampling strategies, SPC controls, Cp/Cpk, GR&R, and integrate Minitab/JMP analytics into daily operations
Apply large-scale data analytics to supplier Track-and-Trace datasets, enabling predictive insights and real-time defect containment
Define and maintain dashboards for defect trends, supplier performance, and yield bridges
Collaborate with SQE teams to develop AI-assisted analytics tools linking supplier OQC to factory IQC datasets
Build deep traceability systems and lead supplier trend analysis to facilitate closed-loop corrective action and predictive component analytics
Execute supplier readiness-to-IQC correlation and MSA capability studies
communicate results throughout the supply chain
Drive proactive engagement with suppliers (SQE) and internal manufacturing quality teams (MQE) to ensure supply continuity at scale
Drive defect prevention through structured inspection processes, escalation protocols, and supplier feedback loops (MSA/GR&R correlation capability studies)
Partner with strategic partners and customer engineering to ensure specifications are measurable and inspectable
Collaborate with Supply Chain and Manufacturing Quality to align incoming inspection readiness with production ramps
Present IQC performance, readiness, and improvement actions to executive leadership
Requirements:
Advanced degree in Engineering, Computer Science, Data Science, or a related field
8+ years in incoming inspection, supplier quality, or metrology leadership in high-volume, high-complexity industries
Demonstrated expertise in statistical and analytical methods such as regression analysis, correlation analysis, DOE, SPC, PFMEA, Gauge R&R, and commonality studies using software tools such as Minitab, JMP, etc.
Strong expertise in dimensional measurement (GD&T, CMM), leak/flow validation, and/or electrical signal integrity testing
Experience managing incoming inspection processes within EMS/ODM environments or similarly complex supply chains
Proven success in building and managing inspection labs, including equipment qualification, calibration systems, and documentation
Deep knowledge of SPC, GR&R, ISO/TL9000, and advanced supplier quality methods
Deep & Wide working knowledge and use of SPC tools (Minitab/JMP) and regression/correlation analysis and SPC analytics