Design the event instrumentation for Pickup at scale

Problem Statement Description

Product context: DoorDash is a local commerce and delivery platform; its products include restaurant delivery, DashPass, grocery and retail delivery, merchant tools, and dasher tools.

DoorDash Pickup lets customers place orders through DoorDash and collect them directly from merchants, often appealing to price-sensitive users who want convenience without delivery fees. In this interview, you are asked to design the event instrumentation needed to understand, operate, and improve Pickup at scale across the customer app, merchant systems, order platform, and post-order experience.

Focus on what should be tracked, where events should be emitted, how data should flow across client and backend systems, and how teams would use that instrumentation to diagnose funnel issues, merchant readiness problems, order accuracy, customer confusion, and operational reliability. The scope is not to redesign Pickup itself, but to define the measurement and observability foundation that enables product, engineering, operations, and merchant teams to make better decisions.

Your design should account for a marketplace environment where customer intent, merchant acceptance, food preparation, customer arrival, handoff, cancellation, refund, and support interactions may happen across different systems and at different times. Consider how instrumentation quality, latency, privacy, and consistency affect DoorDash’s ability to scale Pickup reliably.

The experience should consider:

- Key Pickup user journeys, including discovery, cart, checkout, order confirmation, preparation, arrival, handoff, and post-order support.

- Event taxonomy, naming conventions, required properties, timestamps, identifiers, and relationships between customer, order, merchant, store, and device data.

- Client-side versus server-side instrumentation trade-offs, including reliability, deduplication, offline behavior, and event ordering.

- APIs, data pipelines, analytics tables, dashboards, alerts, and observability needs for product and operational teams.

- Privacy, security, consent, and data minimization considerations, especially around location, payment, customer identity, and merchant operations.

- Cohorts and segmentation such as new versus returning Pickup users, price-sensitive customers, merchant type, geography, store density, and order category.

- Rollout and validation approach, including testing, schema governance, backfills, migration from existing events, and monitoring for broken instrumentation.

- Product trade-offs between comprehensive tracking, engineering complexity, app performance, merchant burden, and decision usefulness.

The goal is to describe a scalable technical instrumentation plan that would help DoorDash measure the Pickup experience accurately, detect failures quickly, and support future product and operational improvements without prescribing the product changes themselves.