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5 дней назад

Platform Analytics Engineer (Machine Learning)

180 000 - 270 000$
Формат работы
onsite
Тип работы
fulltime
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Platform Analytics Engineer (Machine Learning): Building analytics architecture, telemetry pipelines, and data products that turn hardware and software events into actionable insights with an accent on fleet health, reliability, and performance analysis. Focus on designing scalable ETL, evaluating machine learning models, detecting anomalies, and supporting hardware engineering decisions with complex systems data.

Location: Santa Clara, California, United States; primarily in-office

Salary: $180,000–$270,000 USD annual base salary, with potential incentive pay and/or equity.

Company

hirify.global is reshaping the data storage industry through hardware and software platforms, including FlashArray and FlashBlade.

What you will do

  • Define, build, deploy, and evolve analytics architecture and tools for capturing and processing hardware and software events at scale.
  • Design and maintain data pipelines and ETL workflows that transform raw telemetry into reliable, analysis-ready datasets.
  • Apply data analysis and statistical techniques to improve hardware performance, reliability, robustness, and software releases.
  • Build dashboards, alerts, and data products that support data-driven engineering and leadership decisions.
  • Lead data-driven projects and programs from exploration through delivery in partnership with hardware and software engineering teams.
  • Analyze complex systems data and recommend decisions and strategy to hardware engineering leadership.

Requirements

  • Hands-on experience with Python in data, analytics, or similar engineering environments.
  • Strong SQL skills, including complex queries and work with large datasets.
  • Experience with databases; Snowflake or similar analytical databases is highly desirable.
  • Experience with Git, Jenkins, Airflow or similar orchestration tools, CI/CD, and Docker.
  • Industry experience implementing machine learning models, including evaluating accuracy and precision, understanding feature impact, and comparing algorithms.
  • Statistical analysis experience, such as anomaly detection and data quality assessment, plus strong communication and collaboration skills.

Culture & Benefits

  • Collaborative and inclusive environment supporting diverse backgrounds, perspectives, and career paths.
  • Focus on innovation, critical thinking, and challenging technical work.
  • Growth support and opportunities to contribute to meaningful products.
  • Flexible time off, wellness resources, and company-sponsored team events.
  • Employee Resource Groups and inclusive leadership initiatives.

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