Senior Product Manager (Data Platform)

Role Overview

Senior Product Manager for the Enterprise Data Platform. You will own the platform’s long-term vision, strategy, and multi-quarter roadmap, ensuring the platform is reliable, governed, cost-aware, and ready for AI and natural-language analytics.

Responsibilities

• Define and refine the platform vision and product strategy, aligned to business goals and broader roadmaps
• Own the platform capability and governance charter
• Manage the data consumption experience for internal teams and stakeholders
• Ensure AI and natural-language analytics readiness, including guardrails, accuracy, latency, and trust
• Lead migration and legacy sunset efforts
• Drive delivery predictability, execution discipline, and multi-quarter planning across teams
• Improve platform health, adoption, and measurable business outcomes
• Partner with engineering and enable stakeholders through partnership and enablement

Key Missions

• Balance foundational platform work (architecture/services) with high-impact use cases
• Define what it means for a data product to be reliable and production-ready, including classification and protection of sensitive information

Requirements

• Demonstrated success owning end-to-end data/platform products: discovery → requirements → launch → adoption → measurable impact
• 5+ years owning data or analytics products (data-product management strongly preferred)
• 5+ years working with data engineering/data platform/analytics teams, ideally in complex, multi-system environments
• Experience with BI/self-service analytics tools and how they consume platform data, including governance, performance, and cost
• Working knowledge of data governance, classification, access control, and data quality/observability on shared platforms
• Hands-on exposure to AI-assisted or natural-language analytics tools, with judgment about governed data and responsible guardrails
• Strong SQL and comfort analyzing data and platform metadata (logs, cost, usage) and data-observability signals
• Deep familiarity with modern cloud data-warehouse/lakehouse architectures and ELT/transformation patterns
• Experience with modeling frameworks and semantic/metrics layers supporting AI and natural-language analytics
• Excellent communication and stakeholder management, able to explain technical concepts (including AI/NL analytics) to non-technical audiences
• Ability to build and execute multi-quarter, multi-team plans and make trade-offs in an agile environment
• Familiarity partnering with data science/ML teams and supporting needs on shared platforms (access, performance, monitoring)
• Ability to design/evaluate natural-language analytics flows and measure quality, latency, and trust

Nice-to-have

• Experience owning platform- or infrastructure-adjacent data products
• Familiarity with modern AI-powered data-platform patterns (e.g., semantic layers, retrieval-augmented approaches)