Data-Driven Process Reliability Engineer | Drive Robust Manufacturing

Agilent; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Agilent Technologies is seeking a highly driven Process Reliability Engineer to own and elevate the reliability of manufacturing processes in a sustaining production environment. This role eliminates systemic process risks, drives robustness, and sustains high capability manufacturing performance across the site.

You will analyze field and manufacturing data to identify risks, translate insights into targeted improvements, and lead cross‑functional teams to implement prevention, PFMEA-driven

Qualifications

• Bachelor's or Master's degree in Engineering or equivalent.
• Minimum 5 years of experience in manufacturing, process engineering, quality, or reliability engineering.
• Proven PFMEA, control plans, and SPC (Cp/Cpk) implementation to sustain process capability.
• Strong root cause analysis and structured problem solving skills.
• Analytical mindset with ability to translate data into improvements.
• Effective stakeholder management across cross-functional teams.
• Commitment to process discipline, prevention, and compliance.

Responsibilities

• Reduce process risk and strengthen governance with PFMEA and control plans.
• Analyze manufacturing data to identify gaps and drive improvements.
• Lead cross-functional team on field issues and monitor process performance.

Skills

Root cause analysis

Structured problem solving

Data interpretation

Stakeholder management

Cross-functional collaboration

Education

Bachelor's or Master's degree in Engineering or equivalent

Tools

PFMEA

Control plans

SPC Cp/Cpk

Job description

Agilent Technologies is seeking a highly driven Process Reliability Engineer to own and elevate the reliability of manufacturing processes in a sustaining production environment. This role eliminates systemic process risks, drives robustness, and sustains high capability manufacturing performance across the site.

You will analyze field and manufacturing data to identify risks, translate insights into targeted improvements, and lead cross‑functional teams to implement prevention, PFMEA-driven