Senior Product Manager, Data Platform (Remote)

We are looking for a Senior Product Manager to own our Enterprise Data Platform as a product — its capabilities, reliability, governance, cost, and readiness for AI and natural-language analytics. You will own the long-term vision, strategy, and multi-quarter roadmap for the platform, and you will own it end to end: not only the underlying capabilities, but how they show up for the people who consume them.

Today that means internal teams — finance, operations, growth, product, and analytics — building reporting, self-service, data products, and AI and natural-language experiences on a single trusted foundation. The platform is being built so that customer-facing data products are a natural next step rather than a re-platforming.

Your first major focus is our Enterprise Data Hub: consolidating fragmented, legacy data into governed, business-ready data products; driving their adoption; and sunsetting the legacy environment they replace. You will partner closely with data platform engineering and data architecture to deliver the next wave of platform capabilities — most importantly, the foundations that let the platform safely and reliably power AI-enabled and natural-language analytics across reporting, self-service, and data products.

You will operate as the product owner of “what and why,” with engineering and architecture owning “how.” You will treat the platform as a product with real users, real adoption, and real return — measured against a clear North Star.

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, so the same foundation serves them without forcing parallel, ungoverned pipelines.
• 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. Codify policy into the platform rather than into documentation, and define the lightweight “definition of done” every data product meets before it ships.
• 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 — so consumption is fast, safe, and genuinely self-serve, and so teams stop rebuilding shadow models off ungoverned data.
• 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 so the business can rely on the answers these tools produce. Ensure the same foundation meets machine-learning and data-science needs — reliable data access, performance, and monitoring.
• 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 — recognizing that some legacy will run in parallel during the transition. Sequence the work by business domain.
• 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 rather than open-ended task lists, make trade-offs across value, effort, risk, and timing explicit, and keep dependencies and risks visible in integrated plans.
• 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, the consumption model, and the cost of running legacy and the new platform in parallel.
• 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, and adjust the roadmap accordingly.
• 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, and use it to tell the platform’s story to leadership.
• 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 — its architecture, capabilities, constraints, and data flows. Raise the bar for data-platform product management: enable data product managers and partners to define products against the architecture, evolve platform product practices, and mentor others to “think in products.”
  

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.
• 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.
• 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.
• Familiarity partnering with data-science and machine-learning teams and supporting their needs on a shared platform (data access, performance, and monitoring).
• 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.
• 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.
• Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.
• 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) 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.

The national total target cash compensation range for this position, including base salary and bonus target, is $161,000–$213,000 annually.*

*Please note: Final offer amounts are determined by multiple factors, including prior experience, expertise and region & may vary from the amount above. This range does not represent additional compensation benefits (such as equity, 401K or medical, dental or vision insurance).