ezCater is the #1 food tech platform for workplaces in the US, simplifying food management for organizations. They are seeking a Senior Product Manager to lead their Enterprise Data Platform, focusing on its capabilities, governance, and readiness for AI and analytics, while ensuring its alignment with company goals and user needs.

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. Codify policy into the platform rather than into documentation, and define the lightweight “definition of done” every data product meets before it ships
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
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
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
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
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
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
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. 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.”

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

Product Manager

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