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.

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📌 Ee quality engineer: data mining & field reliability (México)
🏢 Segula Technologies
📍 México