Senior Data Product Manager

Who you are

• 7+ years working in or directly with data engineering, data platform, analytics, or data-product teams, ideally in complex, multi-system environments
• 5+ years owning data or analytics products, with direct data-product-management experience strongly preferred
• Demonstrated success owning end-to-end data products — from discovery and requirements through launch, adoption, and measurable business impact — ideally including work on shared or foundational data assets
• Experience defining a data domain from the ground up: canonical or domain modeling, event and entity design, and metrics or KPI architecture that rolls up and drills down cleanly
• Deep familiarity with modern cloud data-warehouse and lakehouse architectures, ELT and transformation patterns, and modeling frameworks and semantic and metrics layers
• Strong SQL and the comfort to explore data and metadata — usage, quality, lineage, cost — yourself, to validate requirements, debug issues, and size opportunities
• Experience partnering with data-science and machine-learning teams and supporting their needs on a shared foundation: reliable data access, the right signals and features, performance, and monitoring
• Working knowledge of data governance, classification, access control, and data-quality and observability practices
• Proven ability to build and execute multi-quarter, multi-team plans and to make and communicate trade-offs across competing initiatives; solid delivery discipline, including tracking progress against estimates
• Excellent communication and stakeholder management — able to explain data and modeling concepts to non-technical audiences, influence senior leaders, and work across engineering, architecture, operations, analytics, 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
• Experience in logistics, last-mile delivery, marketplace, food, or fulfillment domains, and familiarity with the operational realities of delivery (dispatch, routing, tracking, driver and partner dynamics)
• Experience standing up the data foundation for models and decisioning systems — routing, risk scoring, forecasting, or recommendation
• Familiarity with modern AI-powered data-platform patterns (semantic layers, retrieval and search, conversational analytics) and how they change how people consume data
• Experience building on top of, or migrating onto, a governed enterprise data platform and semantic layer

What the job involves

• 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
• 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 benefits from its governance, cataloging, and semantic layer rather than reinventing them
• Partner and enable. Operate as a peer to engineering and architecture and as the connective tissue across delivery operations, analytics leaders, the principal initiative lead, and business stakeholders. Be the authoritative expert on the fulfillment data domain — its model, capabilities, constraints, and data flows

Benefits

• Unlimited Vacation
• Stock Options
• Medical & Dental Insurance
• Subsidized MBTA Passes
• 401k Match
• Stocked Kitchen
• Company Outings
• Parental Leave
• Cupcakes