Senior Product Manager, Data Platform (Remote)

ezCater is the #1 food tech platform for workplaces in the US, providing solutions for managing food needs. They are seeking a Senior Product Manager to own their Enterprise Data Platform, focusing on its capabilities, reliability, governance, and readiness for AI and natural-language analytics.

Responsibilities:

• 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
• 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
• 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
• 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
• Ensure the platform’s governed, semantic models are the grounding layer for AI and natural-language analytics
• 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
• Decompose work into small, estimable data-product units that ship on the order of a week once defined
• Own platform health as a product promise — freshness and success service levels, availability, and fast detection and resolution of data incidents through strong observability
• Treat adoption as the job, not an afterthought
• 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
• Operate as a peer to engineering and architecture, and as the connective tissue across embedded data product managers, analytics leaders, governance, and business stakeholders

Requirements:

• 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
• 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