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EE Quality (Data Mining) Engineer

EE Quality (Data Mining) Engineer

  • CDI
  • Temps plein
  • Au moins 5 ans d'expérience
  • BUT, Licence, Bac+3
  • DATA ANALYST

Mission

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.

Profil

Qualifications & Requirements

  • Education: Bachelor’s degree in Electrical Engineering, Computer Science, Data Science, Automotive Engineering, or a related technical discipline.

  • Experience: 3+ years of experience in E/E quality engineering, vehicle systems diagnostics, or automotive data analytics.

  • Data Mining & Analytics Skills:

    • Proficient in SQL for database queries and data extraction.

    • Strong programming capabilities in Python or R for data wrangling, statistical analysis, and script automation (using libraries like Pandas, NumPy, Scikit-Learn).

    • Experience with data visualization tools (Power BI, Tableau).

  • Automotive E/E Knowledge:

    • Solid understanding of automotive E/E architectures, ECUs, sensors, actuators, and communication protocols (CAN, LIN, Ethernet, FlexRay).

    • Familiarity with OBD-II diagnostics, UDS (ISO 14229), and Diagnostic Trouble Code (DTC) structures.

    • Experience interpreting vehicle log files using tools like Vector CANoe/CANalyzer, Wireshark, or similar.

  • Languages: Business fluency in English (written and spoken).

Preferred Qualifications

  • Experience working with OEM telematics platforms, cloud storage (AWS, Azure, GCP), or big data frameworks (Apache Spark, Databricks).

  • Knowledge of ASPICE, ISO 26222 (Functional Safety), or ISO 21434 (Cybersecurity).

  • Six Sigma Green/Black Belt certification.

Key Competencies

  • Analytical Curiosity: Driven to dig deep into unstructured data to uncover hidden system bugs and trends.

  • Translational Communication: Ability to articulate complex data insights into clear engineering and business terms for non-data science stakeholders.

  • System-Level Mindset: Capable of viewing failures holistically—connecting hardware, embedded software, and network interactions.