Senior Manager, Data Product Strategy & Analytics

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 The Senior Manager, Data & Analytics Products owns a portfolio of enterprise-critical Tier 1 products from strategy and discovery through delivery, adoption, service performance, and value realization. The role connects executive and business priorities to product roadmaps and leads across product, data engineering, analytics, governance, architecture, security, operations, and business teams. The ideal candidate is a data and analytics leader with strong product judgment: commercially grounded, technically fluent, practical in the application of AI, and skilled at aligning senior stakeholders and global delivery teams. Success requires the ability to make enterprise data trusted, analytics products useful, and complex strategic programs executable - without requiring hands-on engineering or model development. WHAT YOU'LL DO Tier 1 insights product portfolio strategy. Own the vision, value proposition, target users, product criticality, investment priorities, outcome metrics, and multi-horizon roadmaps for the company's most important data and analytics products. Balance run, grow, and transform priorities across the portfolio. Discovery and product lifecycle. Lead stakeholder interviews, user research, decision-journey and process analysis, and opportunity sizing. Translate important business questions into product requirements, priorities, roadmaps, success measures, and an ongoing improvement agenda. Product reliability and performance. Define business-critical expectations for accuracy, freshness, consistency, usability, adoption, and support. Partner with Data Engineering team on availability, incident response, and technical remediation while maintaining transparent product-health reporting. Trusted data and governance. Establish clear ownership, shared definitions, lineage, and quality expectations for each product. Partner with Data Governance, Security, Privacy, and IT to ensure access, retention, and control requirements are appropriately addressed. Advanced analytics and applied AI. Identify where predictive analytics, generative AI, intelligent decision support, or automation can improve a user outcome. Shape prototypes and scalable solutions with clear evaluation measures, monitoring, explainability, controls, and human oversight. Executive priorities and strategic execution. Act as a trusted execution partner to senior leaders. Translate priorities into…