About the role
ezCater is the #1 food tech platform for workplaces in the US. The company makes it easy for any organization to manage its food needs and order from over 125,000 restaurants nationwide. For workplaces, ezCater provides flexible and scalable solutions for everything from employee meal programs to one-off meetings, all backed by 24/7 service and business-grade reliability. For restaurant partners, ezCater helps grow their business by bringing them new high-value customers and large orders. We are looking for a Senior Data Product Manager to own the data foundation behind how ezCater fulfills and delivers catering orders. Fulfillment is one of the most data-rich and operationally complex parts of our business, spanning demand and capacity signals, partner and driver assignment, pickup and delivery execution, and the events and telemetry generated at every step across internal systems and external delivery partners. This is a build-from-the-ground-up role on a new, senior-sponsored initiative. You will own the data asset for fulfillment as a product — the canonical model of the domain, the metrics architecture on top of it, and the governed data products and signals that power analytics and the models behind intelligent routing, risk scoring, and service-level decisions. You will own the “what and why,” with engineering and architecture owning the “how.” You will work alongside a principal product lead for the overall initiative and a dedicated engineering team, and partner closely with delivery operations, analytics, data platform engineering, and data architecture. Your first job is to turn a complex operational domain into a clear, trusted, extensible data foundation that every downstream capability compounds on.
What You'll Do: Own the fulfillment data asset as a product. Define and continuously refine the vision and strategy for the fulfillment data domain. Treat it as a product with real users, real adoption, and real return — measured against a clear North Star. Connect it to the broader Enterprise Data and company roadmaps. Own the canonical model and its evolution. Take ownership of the canonical fulfillment lifecycle model — events, entities, relationships, and the happy, alternate, and exception paths — and evolve it from a V1 definition into the authoritative reference the whole initiative builds against. Partner with a business analyst and subject matter experts to get it right, then keep it current as the domain grows. Own the metrics architecture. Define the metrics architecture on top of the canonical model —operational, capability, geographic, coverage, and performance views that roll up to network-health signals and drill down to micro-moments across the order lifecycle. Anchor it on the metrics that matter most: customer-facing reliability, timeliness, accuracy, and total cost to serve. Build governed, trusted data products. Own the definition of what makes a fulfillment data product trusted and production-ready, and deliver the P0 analytics and platform data products the rest of the initiative depends on. Design for multiple consumption paths — analytics and self-service, direct query, and the telemetry and signals that flow back into models. Feed the models that power the platform. Ensure the data foundation reliably serves the machine-learning and decisioning workloads behind intelligent routing, pickup-time prediction, risk scoring, and service-level differentiation. Define the signals, telemetry, and feedback loops these models need, and the contracts for delivering them. Drive delivery and predictability. Decompose work into small, estimable data-product units. 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 reliability, quality, and cost. Treat data health as a product promise — freshness, accuracy, and success service levels, with strong observability and fast resolution of data incidents. Be mindful of the unit economics of what you build. Drive adoption and outcomes. 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. Connect to the broader data platform. Frame this work as a domain built on the Enterprise Data Hub and its governed semantic layer, not a parallel stack. Partner with the data platform team so the fulfillment domain lands on the shared foundation and