- Own end-to-end customer quality for major accounts across qualification, pilot, and sustaining phases.
- Lead complex customer escalations with strong emphasis on Failure Analysis (FA) management, corrective action closure (8D), and customer-facing technical communications.
- You will serve in a customer facing CQE role, supporting OEM, Cloud, and channel customers while partnering closely with design, reliability, factory engineering, released product teams, worldwide FA labs, and customer teams to drive an exceptional total customer experience.
- Operate with significant autonomy, drive cross-functional alignment, and strengthen CQE operating rhythm and artifacts (trackers, repositories, lessons learned, readiness documentation) so work products are repeatable, auditable, and resilient to transitions.
- Core Customer CQE Responsibilities:
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- Serve as the CQE lead for assigned major customer account(s), owning quality execution across qualification, pilot, and sustaining phases.
- Drive qualification readiness and execution (Pilot/RTS), including execution tracking, risk assessment, and crisp status reporting to internal stakeholders and customers.
- Own customer-facing Failure Analysis (FA) requests end-to-end: prioritize, route, track turnaround time, challenge conclusions as needed, and synthesize findings into clear, data-backed narratives for internal/external audiences.
- Lead and author customer corrective action responses (8D or customer-required formats), coordinating cross-functional teams through containment, root cause, corrective action verification, and sustained closure.
- Develop and present customer communication packages that clearly articulate issue description, scope/impact assessment, containment, risk evaluation, and action plans; maintain action trackers and ensure closure discipline.
- Proactively identify emerging risks from qualification/field signals and drive cross-functional mitigation plans before they become customer escalations
- Data & AI-Enabled CQE Responsibilities:
- Build and maintain analytics pipelines for quality signals (qualification logs, FA outputs, reliability metrics, integration/field data, and customer telemetry) to identify trends, outliers, and emerging risks.
- Create lightweight automation and tools (Python, SQL, dashboards) to reduce manual effort in recurring CQE workflows such as log parsing, data preparation, reporting, and action tracking.
- Apply statistical reasoning to support reliability and quality narratives, including trend analysis, failure‑rate concepts, confidence in conclusions, and appropriate handling of small or incomplete data sets.
- Use Generative AI responsibly to accelerate CQE work products (e.g., drafting technical summaries, synthesizing FA findings, proposing next‑step experiments, generating first‑pass 8D content), while validating outputs against source data and complying with data classification and IP protection rules.
- Standards, Process Rigor, and Best Practices:
- Apply structured problem‑solving methodologies (8D, closed‑loop corrective action discipline, root cause analysis) and support issue tracking through verified closure.
- Develop working knowledge of enterprise storage qualification expectations, endurance and reliability definitions, and how test results translate to customer risk.
- Perform and validate log and telemetry analysis using approved internal tools; ensure supporting evidence is properly captured and linked in tracking systems for cross‑team visibility.
- Demonstrate strong data stewardship, documentation discipline, and audit readiness in all customer-facing quality activities; ensure traceability from conclusions to evidence.
- Drive continuous improvement to CQE processes (FA prioritization, tracker hygiene, readiness documentation, lessons learned) and influence adoption across teams and sites