Quality Early Detection Engineer

About the role

The Quality Early Detection Engineer is responsible for monitoring, improving and reporting key quality metrics across the vehicle electrical ecosystem. You will work closely with cross‑functional teams to identify root causes, drive corrective actions and ensure a high level of customer satisfaction.

Key responsibilities

• Handle and improve main quality metrics such as R/1000, CPU and NPS.
• Lead the creation of a unified quality agenda, consolidating issues from customer claims, DTC analysis, service fixes, dealer handling and warranty spend.
• Represent the organization in various quality forums and present findings.
• Investigate, Pareto‑analyze and share the highest Diagnostic Trouble Code (DTC) contributors.
• Organize quality symptoms into documented Problem Objects and manage them through Jira 8D tools.
• Proactively gather peer support to resolve customer‑impacting issues quickly.
• Propose and implement methods to reduce manual effort in quality escalation management.
• Submit monthly quality reports with detailed action plans and status updates.

Required profile

• Bachelor's degree in Electrical Engineering.
• 2+ years experience with software analysis/testing, CAN‑bus technology and log review/root‑cause analysis.
• 2+ years experience using engineering tools, databases and defect‑tracking systems (e.g., Diagnostic Engineering Tool, IVS, VADR, JIRA).
• 2+ years experience with service tools/applications such as FDRS, PTS and workshop manuals.
• 2+ years experience applying quality methodologies (Root Cause Analysis, 8D, Fishbone, 5‑Why, etc.).
• Proven ability to collaborate with upper management and cross‑functional peers.
• Preferred: Master's degree, Six‑Sigma Green/Black Belt, knowledge of vehicle electrical architectures and Ford quality tracking resources.

Required skills

• CAN‑bus technology
• Software analysis and testing
• Log review and root‑cause analysis
• Diagnostic Engineering Tool, IVS, VADR
• JIRA (8D workflow)
• FDRS, PTS, workshop manuals
• Quality methodologies (8D, Fishbone, 5‑Why)
• Six‑Sigma tools (DoE, Taguchi, FMA) – preferred