Senior Product Manager, ezCater

• 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

Desirable

• 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
• 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.”