Data & Analytics Manager – Product Analytics

Pure Storage•Santa Clara, CA

22h•Onsite

About The Position

Everpure's Product Business Unit makes bets on what to build, price, and bring to market and this role is how those bets get pressure-tested against evidence. You'll sit directly with Product leaders as their analytics partner, taking questions that arrive ambiguous ("why is adoption flattening on this platform?") and returning frameworks, models, and recommendations that change what the team does next. What makes this different from a reporting role is the ownership: you define the problem, do the analysis yourself, and stay with it through implementation. Comfort using AI in your daily work is essential here. This team expects you to reach for LLM tools to move faster, whether that's accelerating an analysis, making sense of unstructured text, or cutting the time recurring work takes.

Requirements

Advanced SQL and the fluency to work independently across large, messy enterprise datasets in platforms like Snowflake, Databricks, or BigQuery, including CRM data and product telemetry
Strong hands-on modeling range, i.e. forecasting, business modeling, scenario analysis, segmentation, cohort analysis, and root cause investigation, and the judgment to know which approach a given question actually calls for
Day-to-day working use of generative AI and LLMs in analytics work, and a critical eye for when AI output isn't reliable enough to act on
A track record of owning complex analytical initiatives yourself, from first conversation through implementation and measurable outcome, without needing the problem handed to you pre-scoped
Credibility with senior business leaders; you can translate their priorities into analytical work and translate results back in terms they can act on

Responsibilities

Act as the analytics partner to Product BU leaders, owning the insight generation behind Quarterly Business Reviews and other executive business reviews
Take complex, loosely defined business problems end to end — framing and hypothesis through analysis, solution design, executive-ready dashboards, and implementation
Build the forecasting models, business models, segmentation, and scenario analyses that inform decisions on product performance, pipeline, bookings, customer adoption, installed base, and product usage
Use LLM tools to accelerate your own analysis and extract signal from unstructured text like support cases, sales notes, and customer and product feedback, applying judgment about when AI output needs verification before it reaches a decision-maker
Triage incoming analytics requests from Product Managers and stakeholders, set priorities and clear accountability for delivery, and standardize or automate the work that shouldn't need doing twice