SPC Technician: Data-Driven Quality & Process Improvement

Aranda Tooling, LLC; Chino (CA); On-site; USD 60,000 - 90,000; Full time

Benefits offered by this job

401(k)

401(k) matching

Employee assistance program

Health insurance

Life insurance

Paid time off

Parental leave

Vision insurance

Job summary

Aranda Tooling, LLC is seeking an SPC Technician to monitor, analyze, and improve manufacturing processes using statistical methods and quality tools. The role supports data-driven problem-solving and ensures product quality and process stability through control plans, charts, and continuous improvement initiatives.

The ideal candidate will be detail-oriented with experience in manufacturing quality systems, statistical analysis, and data-driven decision making.

Qualifications

• Associate degree in Quality, Engineering Technology, Manufacturing, Statistics, or related field preferred.
• Minimum 2 years in a manufacturing, quality, or SPC-related environment preferred.
• Familiarity with IATF 16949, ISO 9001, or similar quality management systems preferred.

Responsibilities

• Collect process and quality data from production equipment, inspection systems, and testing devices.
• Analyze production data using SPC methodologies and statistical tools such as control charts, histograms, Cp/Cpk, Pareto, and trend analysis.
• Develop, maintain, and monitor SPC control charts for critical process characteristics; ensure real-time monitoring and reporting.

Skills

SPC principles

Cp/Cpk analysis

Root Cause Analysis

CAPA

MS Excel

Gage R&R

statistical software

data interpretation

communication

Education

Associate degree in Quality or Engineering Technology

Tools

MS Excel

Gage R&R

Statistical software

Job description

Aranda Tooling, LLC is seeking an SPC Technician to monitor, analyze, and improve manufacturing processes using statistical methods and quality tools. The role supports data-driven problem-solving and ensures product quality and process stability through control plans, charts, and continuous improvement initiatives.

The ideal candidate will be detail-oriented with experience in manufacturing quality systems, statistical analysis, and data-driven decision making.