Senior Product Manager (Data Platform)

Join our team as a Senior Product Manager for our Enterprise Data Platform. You will own the long-term vision, strategy, and multi-quarter roadmap for the platform, focusing on its capabilities, reliability, governance, cost, and readiness for AI and natural-language analytics. You will work closely with internal teams and data platform engineering to deliver the next wave of platform capabilities. Your responsibilities will include defining the platform's vision and product strategy, owning the platform's capability and governance charter, managing the consumption experience, ensuring AI and natural-language readiness, leading migration and legacy sunset efforts, and driving delivery and predictability. You will also be responsible for platform health, adoption and outcomes, and partnership and enablement.

• Définir et affiner la vision et la stratégie du produit, en les alignant sur les objectifs de l'entreprise et en les connectant aux feuilles de route plus larges.
• Équilibrer le travail fondamental, l'évolution de l'architecture, les services de plateforme fiables et évolutifs, et les cas d'utilisation à fort impact.
• Posséder la définition de ce qui rend un produit de données fiable et prêt pour la production, y compris la classification et la protection des informations sensibles.
• Demonstrated success owning end-to-end data or platform products — from discovery and requirements through launch, adoption, and measurable business impact — ideally including reliability, cost, or scalability work on a shared platform
• 5+ years owning data or analytics products, with direct data-product-management experience strongly preferred; experience owning platform- or infrastructure-adjacent data products is a plus
• 5+ years working in or directly with data engineering, data platform, or analytics teams, ideally in complex, multi-system environments
• Experience with business-intelligence and self-service analytics tools and how they consume data from a platform, including governance, performance, cost, and how they participate in AI and natural-language analytics
• Working knowledge of data governance, classification, access control, and data-quality and observability practices on a shared platform
• Hands-on exposure to AI-assisted or natural-language analytics tooling, with the judgment to ground answers in governed data and reason about guardrails, accuracy, latency, and trust
• Strong SQL and the comfort to explore data and platform metadata — logs, cost, usage — and data-observability signals yourself, to validate requirements, debug issues, and size opportunities
• Deep familiarity with modern cloud data-warehouse and lakehouse architectures, data lakes, and ELT and transformation patterns, and with modeling frameworks and semantic and metrics layers that can support AI and natural-language analytics
• Excellent communication and stakeholder management — able to explain platform and architectural concepts, including AI and natural-language implications, to non-technical audiences, influence senior leaders, and work seamlessly across engineering, architecture, analytics, governance, and the business
• A disposition that is friendly, flexible, pragmatic, and curious, with a desire to learn something new every day and to raise the bar for the broader data, platform, and product teams
• Proven ability to build and execute multi-quarter, multi-team plans, and to make and communicate trade-offs across competing initiatives; solid delivery discipline in an agile environment, including tracking progress against estimates and velocity
• Familiarity partnering with data-science and machine-learning teams and supporting their needs on a shared platform (data access, performance, and monitoring)
• Designing and evaluating natural-language analytics flows — grounding answers in governed data and measuring quality, latency, and trust
• Familiarity with modern AI-powered data-platform patterns (semantic layers, retrieval and search, conversational analytics, or agentic workflows) and how they reset expectations for how people discover and consume data
• Experience sunsetting a legacy data environment in favor of a governed platform, including reconciliation and parallel-run cutovers