Customer Quality Engineer (CQE) - Early Career (Data & AI Focus)

Seagate Technology•Longmont, CO

20h•Onsite

About The Position

As a Customer Quality Engineer (CQE) in the US Design Center, you will play a critical role in delivering excellent Total Customer Experience by managing customer quality across qualification, pilot, and sustaining phases. This is a customer‑facing CQE role supporting OEM, Cloud, and Channel customers. The role focuses on external‑facing quality analysis, customer communication, readiness documentation, and cross‑functional coordination, partnering closely with Design, Reliability, Factory Engineering, and Customer teams. In addition to core CQE responsibilities, this role emphasizes data‑driven decision making and responsible use of AI to improve speed, consistency, and insight in quality workflows.

Requirements

Motivated self‑starter with strong ownership, attention to detail, and ability to operate effectively in customer‑facing technical situations.
Strong written and verbal communicator who can explain complex technical and quality topics clearly to both internal teams and external customers.
Data‑first mindset: comfortable working with messy, high‑volume datasets and challenging conclusions until evidence supports them.
Able to communicate uncertainty, assumptions, and confidence in conclusions—not just outcomes.
Practical and responsible AI user who understands the importance of validation, data governance, and IP protection
Coursework or academic projects demonstrating data analysis, programming (Python preferred), and applied statistics.
Strong written and verbal communication skills suitable for customer‑facing technical work.
BS in Electrical Engineering, Mechanical Engineering, Computer Engineering, Data Science, Industrial Engineering, or a related field.

Nice To Haves

Exposure to storage technologies (HDD concepts), reliability engineering, quality engineering, or manufacturing/test environments.
Experience with SQL and data visualization tools (Tableau, Power BI) and/or handling large log or telemetry‑style datasets.
Coursework or projects involving Design of Experiments (DOE), ANOVA, hypothesis testing, or statistical process control.
Familiarity with time‑series analysis or statistical monitoring applied to quality or reliability data.
Understanding of causal reasoning (correlation vs. causation, bias, confounding) to avoid incorrect attribution in failure analysis.
Exposure to uncertainty quantification or probabilistic reasoning in engineering or data science contexts.
Interest in predictive quality, anomaly detection, and AI‑assisted quality workflows.

Responsibilities

Support customer qualification readiness and execution across Pilot and RTS phases, including execution tracking and status reporting.
Track and communicate qualification and field Failure Analysis (FA) status, priorities, and turnaround time with worldwide FA teams; maintain FA trackers and customer‑facing artifacts (data packages, summaries, status updates).
Lead and author customer corrective action responses (8D or customer‑required formats), coordinating cross‑functional teams (Design Center, Released Product Teams, Reliability, Factory Engineering) through closure.
Develop customer communication packages that clearly articulate issue description, impact assessment, containment, risk evaluation, and action plans; maintain action trackers through closure.
Drive closure discipline and institutional learning, ensuring customer issues are properly linked to required tracking artifacts (e.g., JIRA, RQC, lessons learned) to prevent recurrence.
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.
Partner with senior CQEs to improve clarity, rigor, and defensibility of customer‑facing quality communications using data‑backed insight.
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.