Senior Product Manager, Data Platform

What You'll Do

• Platform product strategy and vision. Define and continuously refine the platform’s vision and product strategy, grounded in company and Enterprise Data goals, and connect it to the broader data and company roadmaps. Partner with principal and staff engineers on long-term technical direction and trade-offs so product and technical strategy stay tightly aligned.
• A multi-quarter, multi-team roadmap. Balance foundational work — architecture evolution, trusted and scalable platform services, the semantic and presentation layers, governance, classification and access, cost and observability — with high-leverage use cases across analytics, self-service, and AI and natural-language consumption. Account for machine-learning and data-science workloads as part of the overall strategy.
• The platform’s capability and governance charter. Own the definition of what makes a data product trusted and production-ready: classification and protection of sensitive information, role-based access aligned to classification, validation and contracts between raw and refined layers, a governed semantic and metrics layer, and a catalog that makes data products discoverable with clear ownership, lineage, and definitions.
• The consumption experience, end to end. Own how platform capabilities surface for the people who use them: governed self-service, business intelligence, and AI and natural-language experiences grounded on trusted data. Define the contracts between the platform and its consumers — readiness criteria, service levels, semantic definitions, and serving surfaces.
• AI and natural-language readiness. Ensure the platform’s governed, semantic models are the grounding layer for AI and natural-language analytics. Partner on the evaluation of analytics and AI tooling, and work through guardrails, accuracy, latency, and trust.
• Migration and legacy sunset. Lead the move from the legacy environment onto the platform: reconcile the most depended-on legacy data against trusted sources, plan and resource the cutover with each business area (including user-acceptance testing and the refactoring of downstream reporting), and sunset legacy.
• Delivery and predictability. Decompose work into small, estimable data-product units that ship on the order of a week once defined. Drive credible, dated commitments and milestone-level goals.
• Reliability, operability, and cost. Own platform health as a product promise — freshness and success service levels, availability, and fast detection and resolution of data incidents through strong observability. Own the platform’s unit economics: cost per unit of consumption.
• Adoption and outcomes. Treat adoption as the job, not an afterthought. Validate data products against real usage with their business owners before build, drive adoption and change management, own documentation and enablement, measure business impact.
• The platform’s North Star and metrics. Define, instrument, and report the platform’s North Star and the metric tree beneath, use it to prioritize the roadmap.
• Partnership and enablement. Operate as a peer to engineering and architecture, and as the connective tissue across embedded data product managers, analytics leaders, governance, and business stakeholders. Be the authoritative expert on the platform.

Nice to Have

• 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).
• Experience sunsetting a legacy data environment in favor of a governed platform, including reconciliation and parallel-run cutovers.

What You Have

• 5+ years working in or directly with data engineering, data platform, or analytics teams, ideally in complex, multi-system environments.
• 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.
• 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.
• 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.