Platform Analytics Engineer

THE ROLE

The Platform Analytics team sits within the FlashArray and FlashBlade organization and turns rich telemetry from Everpure’s hardware and software into actionable insights.

Focus on hardware and systems analytics, using data from physical devices rather than traditional business metrics.Help engineering and leadership understand fleet health, reliability, and performance so they can make better product and roadmap decisions.Work closely with hardware, software, and support teams to close the loop between what our systems do in the field and how we design, test, and improve them.Be part of an inclusive, collaborative team that values a diversity of backgrounds, perspectives, and career paths.

WHAT YOU'LL DO

• Define, build, deploy, and evolve our analytics architecture and tools to capture, process, and automate hardware and software events at scale.
• Design and maintain data pipelines and ETL that transform raw telemetry into reliable, analysis‑ready datasets.
• Apply data analysis and statistical techniques to improve performance, reliability, and robustness of our hardware platforms and software releases.
• Build dashboards, alerts, and other data products that enable engineering and leadership to make data‑driven decisions.
• Lead and drive data‑driven projects and programs from exploration through delivery, partnering with hardware and software engineering teams.
• Analyse complex systems data and recommend business decisions and strategy to hardware engineering leadership.
• We are primarily an in-office environment and therefore, you will be expected to work from the {{OFFICE_LOCATION}} office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• Strong, hands‑on experience using Python in data/analytics or similar engineering environments.
• Strong experience using SQL, including writing complex queries and working with large datasets.
• Experience with databases (Snowflake or similar analytical databases highly desirable).
• Experience with modern engineering tooling such as Git and Jenkins/Airflow (or similar orchestration tools) to drive CI/CD with docker.Industry experience implementing machine learning models, including:
  • Evaluating model accuracy and precision.
  • Understanding how features impact model behaviour and performance.
  • Comparing and assessing different ML algorithms for a given problem.Background in statistical analysis (e.g., anomaly detection, data quality assessment) is highly desirable.
• Excellent verbal communication and collaboration skills, with the ability to work across hardware, software, and business stakeholders.

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