Ee Quality Engineer: Data Mining

Hace 1 día

méxico Segula Technologies Jornada completa

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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 (8 D, 5 Whys, Ishikawa, Fishbone ) to drive Permanent Corrective Actions (PCA).

3. Quality Metrics, Dashboards & Reporting

Develop, automate, and manage interactive quality dashboards (e.g., Power BI, 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.

Ee Quality Engineer: Data Mining

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