DoorDash interview questions

Showing 1–30 of 241 results

• Allocate Support Cost and Diagnose DeclineHard

  This question evaluates operational analytics competencies including cost allocation and attribution for support work, root-cause diagnosis of KPI regressions, and metric design for complaint-driver identification and prevention.

  DoorDashAnalytics EngineerAnalytics & ExperimentationTechnical Screen
• Analyze Driver Requests for Food Delivery OrdersMedium

  This question evaluates data manipulation and analytical SQL skills in the Data Manipulation (SQL/Python) domain, focusing on window functions for ranking and temporal comparison, joins, and aggregation when analyzing orders and driver requests.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Answer feedback, conflict, and failure questionsMedium

  This set of behavioral prompts evaluates interpersonal communication, the ability to give and receive constructive feedback, conflict resolution, accountability for mistakes, and motivation for joining the employer.

  DoorDashSoftware EngineerBehavioral & LeadershipOnsite
• Answer rapid-fire behavioral questionsMedium

  Answer rapid-fire behavioral questions evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  DoorDashSoftware EngineerBehavioral & LeadershipTechnical Screen
• Assess Success Criteria for Bike-Courier Delivery LaunchHard

  Evaluates marketplace analytics for launching a bike-courier delivery option in dense delivery markets. Strong answers define stakeholder value, success metrics, market selection, phased rollout, guardrails, and causal evaluation under network effects.

  DoorDashData ScientistAnalytics & ExperimentationTechnical Screen
• Boost App Installs: Analyze and Experiment with Conversion FunnelMedium

  DoorDash analytics prompt on improving mobile web to app-install conversion, covering funnel measurement, attribution, cohorts, install and activation experiments, incentives, guardrails, and long-term retention impact.

  DoorDashData ScientistAnalytics & ExperimentationOnsite
• Bootstrap Service: Aggregate Downstream Services and Return the Right HTTP StatusMedium

  Implement a bootstrap service that calls several independent downstream services concurrently, aggregates their data into one response for a client app, and returns an HTTP status code that reflects partial or total failure. It tests concurrent calls, timeouts, per-section error reporting, and mapping downstream failures to client-facing status codes.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build ETA prediction and simulate impactHard

  This question evaluates machine learning competencies for ETA prediction, including feature engineering, leakage detection, time-based validation, point and quantile modeling, evaluation and calibration metrics, cost-sensitive decisioning, explainability and fairness auditing, and production deployment considerations.

  DoorDashData ScientistMachine LearningOnsite
• Build Resilient Aggregation and Debug RoutingMedium

  This question evaluates skills in building resilient API aggregation, fault-tolerant retry handling and test coverage for transient failures, as well as debugging and improving load-balancer routing policies including adding round-robin distribution and reasoning about consistent hashing.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build Two Interacting Services That Carry Out a Realistic Refund FlowMedium

  A senior coding exercise, with an AI assistant allowed, to build two cooperating services that carry out a refund end to end. It tests service boundaries, a refund state machine, idempotent retries, handling of declines and timeouts, and a runnable simulation of realistic refund scenarios.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build a Delivery Payout Calculation APIMedium

  Build a payout endpoint that retrieves delivery activity and accrues 30 cents per minute for each concurrent order. The solution covers interval sweeps, malformed events, exact money, day boundaries, final rounding, upstream timeouts, retry limits, caching, and layered tests.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Build a Resilient Bootstrap API with Partial Upstream ResultsMedium

  Combine Consumer, Payment, and Address APIs with correct identifier dependencies, partial success, bounded timeouts, and complete response fields.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build a Running Late-Order Refund Workflow as Separate Python ServicesMedium

  Build a continuously running Python system of separate services that executes a small workflow graph in which late orders trigger refunds, then discuss new features, API evolution, adding payments, and testing. It tests scoping under time pressure, service boundaries, idempotent refunds, and effective AI-assisted development.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build a Three-Service Refund Workflow over HTTPMedium

  Implement and orchestrate a multi-service refund pipeline over HTTP, ensuring idempotent execution, safe concurrency limits, and crash recovery across separate process boundaries.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Build a late-delivery risk modelHard

  This question evaluates competencies in production-ready predictive modeling, including target definition, feature engineering, temporal train/validation splitting to avoid leakage, model evaluation and calibration, monitoring and ramp strategies, and translating probabilistic outputs into business-impacting decisions for a Data Scientist.

  DoorDashData ScientistMachine LearningOnsite
• Build an Order-Exception Microservice with Explicit Refund PoliciesMedium

  Design and demonstrate a local service for missing or delayed orders, with explicit refund rules, duplicate handling, and auditable decisions.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Build and Debug a Dasher-to-Order Assignment Service, Then Prepare It for ProductionMedium

  A DoorDash onsite exercise to finish and debug starter code for a service that assigns delivery drivers to orders. It tests constant-time bookkeeping, defending a list versus hash map storage choice, invariant-based testing, and how to make the service safe and scalable in production.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Calculate Courier EarningsMedium

  This question evaluates algorithmic problem-solving with emphasis on temporal interval aggregation, handling overlapping events, and applying conditional rate multipliers under specified business rules.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Calculate Daily Driver PayHard

  This question evaluates event-driven time-interval processing, interval-overlap arithmetic, rate computation, and robustness to malformed, duplicate, or unordered events when aggregating a driver's pay from a day's log.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Calculate Driver PaymentsMedium

  Implement a payout calculation system for delivery drivers by deduplicating order logs, merging overlapping active-time intervals, and computing base pay, delivery bonuses, and tips.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite
• Calculate Late Delivery Percentage and Top CustomersMedium

  This question evaluates data manipulation and aggregation skills for a Data Scientist role, focusing on date handling, computing the percentage of late orders, and ranking customers using SQL or Python.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Monthly Restaurant Sales GrowthEasy

  This problem tests the ability to aggregate transactional data by month and use window functions to calculate percentage growth between consecutive periods while handling null values.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Calculate Order Request MetricsHard

  This question evaluates SQL and data-manipulation competencies for computing order-level metrics, including aggregations, joins, windowed event sequencing, timestamp/timezone handling, and NULL-aware comparisons.

  DoorDashData ScientistData Manipulation (SQL/Python)Onsite
• Calculate power and test durationMedium

  This question evaluates proficiency in statistical power analysis, sample size calculation for proportions, hypothesis testing for two-sided z-tests, and practical experiment duration estimation for A/B testing.

  DoorDashData ScientistStatistics & MathTechnical Screen
• Compare front-end state management approachesHard

  DoorDash software-engineer onsite front-end question: compare Redux with modern state and data management approaches (Redux Toolkit/RTK Query, React Query, SWR, Zustand, Recoil, MobX, Context) and design an end-to-end strategy for a React SPA that needs offline viewing, pagination, optimistic updates, and error retries. It tests separating client UI state from server cache, caching/invalidation, performance, type safety, testing, and an incremental migration plan off Redux.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Compute Courier Delivery PayEasy

  This question tests the ability to handle time intervals and calculate cumulative earnings based on overlapping delivery pay rates.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute Differences Between Catalog TreesMedium

  This question evaluates proficiency in tree algorithms and hierarchical data comparison, including simultaneous traversal, subtree sizing, key-based node identity, and time/space complexity reasoning, and is commonly asked to assess algorithmic problem-solving and trade-off analysis when comparing structured datasets.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute Driver Pay with Double-Pay WindowsMedium

  This question evaluates a candidate's ability to perform interval arithmetic and aggregation for time-based pay computations, including handling overlapping intervals, half-open boundaries, capped double-pay application, and special-case handling for canceled orders.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute Nearest Destination DistancesEasy

  This problem tests your ability to use Multi-Source Breadth-First Search (BFS) to find the distance from every empty cell to the nearest destination in a grid.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Compute cart total with best promotionMedium

  This question evaluates numerical reasoning for monetary calculations, handling of multiple promotion types, precision and rounding considerations, and edge-case handling such as empty carts and clamping totals within the Coding & Algorithms domain.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite