Senior Data Product Manager, Delivery Transformation

Responsibilities

• Own the fulfillment data asset as a product, including its vision, strategy, adoption, and return.
• Define and evolve the canonical fulfillment lifecycle model covering events, entities, relationships, and happy, alternate, and exception paths.
• Define the metrics architecture across operational, capability, geographic, coverage, and performance views.
• Build governed, trusted analytics and platform data products for self-service, direct query, telemetry, and model signals.
• Provide reliable data foundations, signals, telemetry, and feedback loops for intelligent routing, pickup-time prediction, risk scoring, and service-level decisions.
• Decompose work into estimable data-product units and drive dated commitments, milestones, dependency tracking, and trade-offs.
• Own data reliability, freshness, accuracy, service levels, observability, incident resolution, and cost.
• Drive adoption, documentation, enablement, business validation, and measurable impact.
• Connect the fulfillment domain to the Enterprise Data Hub, governed semantic layer, cataloging, and enterprise data governance.
• Partner with engineering, architecture, delivery operations, analytics, data platform, data architecture, and business stakeholders.

Requirements

• 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 preferred.
• End-to-end data product ownership from discovery and requirements through launch, adoption, and measurable business impact.
• Experience defining data domains from the ground up, including canonical or domain modeling, event and entity design, and metrics or KPI architecture.
• Familiarity with modern cloud data warehouses and lakehouse architectures, ELT and transformation patterns, modeling frameworks, semantic layers, and metrics layers.
• Strong SQL and ability to explore data and metadata, including usage, quality, lineage, and cost.
• Experience partnering with data science and machine learning teams on reliable data access, signals and features, performance, and monitoring.
• Working knowledge of data governance, classification, access control, data quality, and observability.
• Ability to build and execute multi-quarter, multi-team plans and communicate trade-offs.
• Strong delivery discipline, communication, and stakeholder management across technical and business audiences.
• Ability to travel up to 5 days per quarter for team gatherings and other events, when applicable.

Nice to Have

• Experience in logistics, last-mile delivery, marketplace, food, or fulfillment domains.
• Familiarity with dispatch, routing, tracking, and driver and partner dynamics.
• Experience building data foundations for routing, risk scoring, forecasting, or recommendation systems.
• Familiarity with AI-powered data-platform patterns, semantic layers, retrieval and search, and conversational analytics.
• Experience building on or migrating onto a governed enterprise data platform and semantic layer.