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DoorDash Business Model & Trade-offs Questions

Analysis of DoorDash's business model within a platform-based marketplace context, including revenue streams (delivery fees, commissions, subscription), cost structure (logistics, driver incentives), partnerships, pricing strategies, market expansion decisions, and the strategic trade-offs between growth, profitability, and delivering value to customers.

HardTechnical
53 practiced
Design the architecture for a forecasting pipeline that predicts GMV 12 months ahead at city-level granularity. Describe feature selection (historical GMV, promotions, weather, macro indicators), model families you would consider, how you would evaluate model accuracy, and how BI would serve forecasts to stakeholders with uncertainty bounds.
MediumTechnical
49 practiced
Given tables users(user_id, channel), orders(order_id, user_id, created_at, total_amount), and marketing_spend(channel, date, spend), describe and write SQL to compute CAC by channel and 12-month LTV per channel cohort. State assumptions and explain how you would align time windows for CAC and LTV.
HardTechnical
74 practiced
Executive leadership asks for a dashboard that surfaces the trade-offs between growth and profitability across markets. Describe what KPIs and visualizations you would include to quantify growth (e.g., order volume, new users) and profitability (e.g., contribution margin, take rate, unit economics), and outline how BI would enable scenario analysis for leaders deciding where to invest.
HardSystem Design
46 practiced
You must choose between batch and streaming architectures for a near-real-time fraud detection pipeline that feeds BI dashboards. Compare batch vs streaming in terms of latency, cost, complexity, data completeness, and operational overhead. Recommend an approach for DoorDash's scale and justify trade-offs.
MediumTechnical
69 practiced
You are asked to evaluate a potential new city for expansion. List the datasets you would require (internal and external), describe the top-line analyses and thresholds you would compute to recommend go/no-go, and outline the dashboard you would build to monitor early performance post-launch.

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