Senior Quality Engineer

LHH; Secaucus (NJ); On-site; USD 110,000 - 140,000; Full time

Job summary

A fast-growing electronics company is seeking a Principal Quality Engineer to lead the development of data-driven quality systems aimed at enhancing reliability and performance across high-volume manufacturing. The role will advance smart-factory initiatives, implementing key technologies like SPC and AI. Candidates should have extensive experience in manufacturing quality and strong leadership skills. Effective communication across various teams is essential for this position, which requires a Bachelor's degree in a relevant field and substantial experience in the industry.

Qualifications

• 10-15 years of experience in high-volume, high-complexity manufacturing.
• 5+ years in leadership or transformation roles.
• Experience with advanced data analysis techniques.

Responsibilities

• Lead transformation initiatives for manufacturing quality improvement.
• Define and implement smart-factory capabilities.
• Collaborate across teams to integrate quality solutions into production.

Skills

Statistical Process Control (SPC)

Regression analysis

Strong communication skills

Data Analysis

Knowledge of automation platforms (Siemens, Allen-Bradley)

Education

Bachelor's degree in Engineering, Computer Science, or related field

Advanced degree in Engineering, Computer Science, or related field

Tools

Minitab

JMP

Python

R

SQL

Job description

A rapidly growing company in the electronics space is seeking to hire a Principal Quality Engineer who will lead the development and deployment of data‑driven quality systems that elevate reliability, reduce variation, and strengthen performance across high‑volume manufacturing lines. This role is central to advancing smart‑factory initiatives, integrating SPC, metrology, automation, AI, and digital traceability to deliver exceptional customer outcomes and world‑class operational excellence. You will collaborate across design, operations, suppliers, and NPI teams to embed predictive quality and ensure robust performance in next‑generation manufacturing environments.

Location: Onsite in Secaucus, New Jersey

Employment Type: Direct hire, full-time

Responsibilities

• Lead or support transformation initiatives that redefine manufacturing risk analysis, quality, and continuous improvement.
• Define and implement systems, processes, and frameworks supporting smart‑factory capabilities, including automation, metrology, advanced inspection, and predictive analytics.
• Collaborate across design, quality, manufacturing, test, and supplier engineering teams to integrate quality solutions into production.
• Develop, implement, and sustain Statistical Process Control (SPC) systems.
• Lead Gauge R&R and measurement system analyses to ensure accuracy and repeatability.
• Translate DFM and PFMEA insights into actionable production‑level risk controls.
• Perform advanced data analysis to identify trends, detect anomalies, and enable predictive quality.
• Lead supplier qualification, audits, and ongoing supplier quality management.
• Implement and maintain data traceability systems to meet compliance and customer requirements.
• Support new customer engagement, including quality requirement reviews and alignment with contract manufacturing standards.
• Drive structured problem‑solving for internal and supplier‑related quality issues.
• Partner with cross‑functional teams to ensure quality during New Product Introduction (NPI).
• Evaluate and deploy AI and automation technologies (machine vision, predictive analytics, anomaly detection, digital twins) to enhance quality and efficiency.
• Ensure compliance with industry quality standards (ISO 9001, IATF 16949, etc.).

Required Qualifications

• Bachelor's degree in Engineering, Computer Science, Data Science, or related field required. An advanced degree in Engineering, Computer Science, Data Science, or related field is highly desired.
• 10–15 years of experience in high‑volume, high‑complexity manufacturing.
• 5+ years in leadership or transformation roles (not necessarily people management).
• Expertise in regression analysis, correlation analysis, DOE, SPC, PFMEA, Gauge R&R, commonality studies.
• Proficiency with Minitab, JMP, Python, R, SQL, or equivalent.
• Demonstrated success in improving yield, reliability, or process robustness.
• Excellent communication skills for both technical and executive audiences.
• Background in electronics assembly, PCBA, servers, or high‑reliability industries.
• Proficiency with CAD tools (SolidWorks, AutoCAD) and industrial automation platforms (Siemens, Allen‑Bradley).
• Experience with PLC programming, robotics integration (6‑axis, gantry), and industrial sensors.
• Familiarity with MES systems and automation scripting using Python or SQL.

Nice to Have

• Experience with AI/ML tools for predictive analytics or anomaly detection.
• MBA or experience in business, finance, or economics.