EE Quality (Data Mining) Engineer

ob Summary

We are seeking an EE Quality Engineer (Systems Data Mining Specialist) to drive advanced field quality, reliability, and defect prevention for Electrical & Electronic (E/E) systems. In this role, you will leverage telematics data, vehicle diagnostic logs (DTCs), over-the-air (OTA) metrics, and warranty databases to mine, analyze, and translate complex vehicle system data into actionable quality improvements.

You will bridge the gap between Big Data analytics and hardware/software systems engineering, detecting emerging quality trends early and collaborating with cross-functional teams to resolve root-cause failures before they impact customer satisfaction.

Key Responsibilities

1. Data Mining & Field Quality Analytics

• Build, execute, and maintain automated data pipelines and queries to extract quality insights from vehicle telematics, Diagnostic Trouble Codes (DTCs), warranty claims, OTA software logs, and customer feedback.

• Apply statistical models, pattern recognition, and machine learning techniques to identify emerging E/E quality risks, software anomalies, and intermittent system behavior in production fleets.

• Establish predictive quality indicators (e.g., early-warning algorithms) to flag potential field issues before traditional warranty reporting triggers occur.

2. EE Systems Root Cause & Technical Analysis

• Partner with Systems Engineers, DREs, and Software Quality Engineers to correlate mined data with root-cause physical or software failure modes (e.g., CAN/LIN/Ethernet bus communication loss, voltage drops, ECU resets, sensors/actuators degradation).

• Analyze raw vehicle bus logs (CAN trace files, PCAP) alongside cloud telemetry to reconstruct system-level failure scenarios.

• Participate in root-cause investigations using structured frameworks (8D, 5 Whys, Ishikawa, Fishbone) to drive Permanent Corrective Actions (PCA).

3. Quality Metrics, Dashboards & Reporting

• Develop, automate, and manage interactive quality dashboards (e.g., PowerBI, Tableau) to visualize E/E system health, warranty trends (R/1000, CPU), and recall risks for executive leadership and engineering teams.

• Monitor software update (OTA) campaign success rates, post-flash error rates, and battery-drain telemetry.

• Present data-backed technical findings during program quality reviews, gate approvals, and supplier quality syncs.

4. Supplier & Cross-Functional Quality Collaboration

• Work with Tier-1 ECU/Software suppliers to reconcile field diagnostic data with component-level test results and manufacturing logs.

• Provide data evidence to validate software bug fixes, EE design revisions, and hardware modifications.

• Contribute to continuous improvement by updating DFMEA, PFMEA, and E/E quality standards based on insights gained from field data mining.