Analyst

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem.

This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.

THE ROLE

We are looking for a senior analyst who builds. Not someone who specs a dashboard and hands it to a BI developer — someone who takes an ambiguous business question, models the data behind it, designs the interface, ships it as a working web application, and then sits with the stakeholder who uses it. You'll work across the business — People, Finance, Products, and other lines of business — building custom, web-based analytics products that people actually open on a Monday morning. You'll use AI and GenAI tools (including Claude) to move from idea to deployed prototype in days rather than quarters, without the constraints of traditional BI platforms. This is a hands-on individual contributor role. You will spend your time in SQL, in data models, in code, and in front of the people who depend on what you build.

WHAT YOU'LL DO

• Own analytics products end to end. Gather requirements directly from business stakeholders, model the underlying data, build the dashboard, validate it, deploy it, and iterate on real usage independently.
• Design and ship custom web-based dashboards. Build intelligent, self-serve analytics experiences as real web applications, using AI and GenAI tools to rapidly prototype and implement solutions that off-the- shelf BI tools can't deliver.
• Make the data trustworthy. Build clean, well-documented data models in SQL. Partner with data engineering and platform engineering so what you ship is scalable, reliable, and audit-ready in production.
• Design for the reader, not the data. Decide what belongs on the screen and what doesn't. Make hierarchy, layout, and chart choice do real work, so an executive gets the answer in five seconds and an analyst can still drill to the detail.
• Turn findings into decisions. Translate complex analysis into a clear narrative for senior leadership — making the story behind the numbers as rigorous as the numbers themselves — and follow through until the insight changes something.
• Prototype where AI helps. Explore and deploy AI-powered analytics capabilities — natural-language querying, automated narrative, anomaly surfacing — where they make a product genuinely more useful.