Katkat

Company

DoorDash interview questions

241 practice questions in the style of DoorDash's interviews, covering Analytics & Experimentation, Data Manipulation (SQL/Python), Behavioral & Leadership, Software Engineering Fundamentals, Machine Learning, Coding & Algorithms, Statistics & Math, System Design, ML System Design.

Questions241

14 easy159 medium68 hard

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DoorDash interview questions by the numbers

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Katkat has 241 DoorDash interview questions. The largest topics are Coding & Algorithms (22%), Analytics & Experimentation (22%) and System Design (19%). By difficulty: 6% easy, 66% medium and 28% hard. The most common round is Onsite (56%). Frequent concepts include System Design, Causal Inference, Distributed Systems and A/B Testing.

Difficulty

  • Easy6% · 14
  • Medium66% · 159
  • Hard28% · 68

Interview rounds

  • Onsite56% · 134
  • Technical Screen43% · 104
  • Online Assessment1% · 3

Roles

  • Software Engineer64% · 154
  • Data Scientist32% · 77
  • Machine Learning Engineer3% · 7
  • Analytics Engineer1% · 2
  • Product Analyst0% · 1

Frequent concepts

  • System Design7% · 16
  • Causal Inference5% · 13
  • Distributed Systems5% · 11
  • A/B Testing4% · 10
  • Experimentation3% · 7
  • Marketplace Analytics3% · 7

Showing 61–90 of 241 results

  • This question evaluates a candidate's ability to demonstrate end-to-end project ownership, technical decision-making, cross-functional communication, and measurement of business impact, and is commonly asked to assess how an individual explains their role, trade-offs, launch outcomes, and lessons learned.

    DoorDashSoftware EngineerBehavioral & LeadershipTechnical Screen
  • A DoorDash software-engineering onsite behavioral question asking you to walk through your most impactful recent project: the problem, your specific role, measurable impact, a key technical decision and its trade-offs, how you handled scaling and reliability, a risk or failure you hit, and what you'd do differently. Includes a structured STAR-plus-deep-dive framework and a worked real-time-dispatch example.

    DoorDashSoftware EngineerBehavioral & LeadershipOnsite
  • DoorDash statistics and experimentation prompt on A/B testing a recommendation algorithm's revenue impact, covering hypotheses, randomization, sample size, heavy-tailed revenue, p=0.08 interpretation, and causal alternatives.

    DoorDashData ScientistStatistics & MathTechnical Screen
  • This question evaluates a candidate's ability to design an API aggregator, covering skills in service orchestration, fault tolerance, latency and SLO management, error handling, and observability for distributed HTTP/JSON services.

    DoorDashSoftware EngineerSystem DesignTechnical Screen
  • This question tests the ability to design an experimentation framework for a two-sided marketplace, focusing on metric definition, causal inference, and identifying potential gaming behaviors in delivery logistics.

    DoorDashData ScientistAnalytics & ExperimentationTechnical Screen
  • This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Experiments to Measure Promotion Scheduling Impact states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

    DoorDashData ScientistAnalytics & ExperimentationOnsite
  • This question evaluates system design competencies such as API design, data modeling and storage selection for aggregated 5-point ratings, handling user-generated posts and likes, service decomposition, consistency trade-offs, scalability, and designing a monthly batch payout pipeline for driver earnings.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design item reviews and votes with purchase eligibility, unique reviews, rating aggregates, reliable counters, pagination, and recoverable caching.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This question evaluates competence in designing scalable, fault-tolerant streaming systems and implementing correct time-windowed aggregation logic, testing skills in distributed systems, stream processing, and data correctness; it belongs to the system design domain and streaming/real-time data engineering and spans both conceptual architectural thinking and practical application-level concerns. It is commonly asked to probe reasoning about correctness under at-least-once delivery and messy inputs (out-of-order, duplicate, late, or malformed events), operational resilience, and trade-offs between throughput, latency, and auditability in production-scale pipelines.

    DoorDashSoftware EngineerSystem DesignTechnical Screen
  • This question evaluates competency in designing scalable, highly available payment-enabled distributed systems, including skills in traffic spike handling, idempotency and reconciliation, real-time totals and leaderboards, latency and throughput planning, and compliance/privacy considerations.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This question tests the ability to design a scalable review system capable of handling high-write volume, media storage, and efficient retrieval with pagination.

    DoorDashSoftware EngineerSystem DesignTechnical Screen
  • Design donation collection and charity payouts with a PSP, idempotent state transitions, durable ledger records, asynchronous provider events, and reconciliation.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a donation system for a fundraising campaign that runs for exactly three days, where donors choose a charity, pay through a payment processor and see running totals. It tests payment idempotency and reconciliation, handling a time-boxed traffic burst, avoiding write hotspots on totals, and fraud protection.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This problem tests the ability to architect a robust, fault-tolerant workflow engine capable of managing complex state transitions and ensuring transactional integrity in distributed systems.

    DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
  • A DoorDash system design prompt to build a food item review system for a delivery app that also rewards customers for reviewing. It tests product judgment on open questions, verified-purchase writes, cached rating aggregates, and designing a practical, abuse-resistant reward program with reliable crediting.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This problem tests your ability to design a distributed, read-heavy rating and review system that handles high concurrency and ensures data integrity.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a food review platform for discovery, ratings, review text, optional dish-level content, and trusted aggregates. Define entity and review contracts, search and read paths, moderation, abuse resistance, privacy, scaling, and operational recovery.

    DoorDashSoftware EngineerSystem DesignOnsite
  • System design question for the review feature of a food-delivery platform, where customers rate and review the food from delivered orders and others browse ratings. The focus is database schema design: tables, keys, constraints, indexes, rating aggregates, and how edits, deletes and moderation keep them correct.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a scalable review and voting platform for dishes and restaurants, focusing on transactional vote changes, aggregate counter consistency, hot-item read/write scaling, and stable pagination.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design an item-level review and rating platform for food delivery that handles high-throughput reads, asynchronous sentiment and moderation pipelines, and near real-time score aggregations.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This question assesses the ability to design an end-to-end, low-latency recommendation system for a food delivery marketplace, focusing on candidate retrieval strategies, real-time personalization, and system architecture.

    DoorDashData ScientistMachine LearningOnsite
  • This question assesses the ability to architect a multi-stage recommendation pipeline that balances low-latency retrieval with high-accuracy ranking in a geo-aware, high-concurrency food delivery context.

    DoorDashData ScientistMachine LearningOnsite
  • A system design question on a menu review feature for a food-delivery platform, where customers rate and review the menu items they ordered. It tests requirement prioritization, detailed review schema and API design, rating aggregation, caching and moderation, and time management that still reaches a deep dive.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a resilient, distributed workflow orchestration engine capable of executing multi-step business processes across heterogeneous services with strict guarantees around durability, idempotency, and failure recovery.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This problem tests your ability to design a high-throughput observability pipeline capable of ingesting, storing, and alerting on time-series metrics from a distributed fleet of services.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a resilient, stateful workflow orchestration engine to process complex order refunds as a Directed Acyclic Graph (DAG), ensuring idempotency, crash recovery, and financial consistency.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a resilient bootstrap aggregation service that fetches critical initial state for a mobile app on launch. Tests interface design, concurrent I/O, partial failure handling, and operational trade-offs.

    DoorDashSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • Design a review and rating system for a food-delivery platform, where customers review the individual menu items they received and every menu shows each item's rating. It tests data modeling, purchase verification, incremental rating aggregation, caching for read-heavy menus, moderation, and handling bursts on popular items.

    DoorDashSoftware EngineerSystem DesignOnsite
  • This problem tests the ability to design a high-throughput, real-time analytics system that maintains a top-k leaderboard over a sliding time window.

    DoorDashSoftware EngineerSystem DesignOnsite
  • Design a personalized store recommendation feed for an on-demand delivery platform and evaluate key machine learning modeling trade-offs.

    DoorDashSoftware EngineerML System DesignOnsite

DoorDash interview experiences

All 45 →
  • Dasher Pay Phone Screen with a Surprise Sweep Line and Peak Windows Follow-upDoorDash · Software Engineer · Technical Screen · Oct 2026

    This was the dasher pay question from the interview reports on the forum. I spent a good chunk of time up front discussing the requirements and the data shape. I wanted to get part 1 done with a simple algorithm first and worry about the rest later, and I had already finished writing it, but the interviewer was clearly unhappy and hinted that I should use a sweep line. Changing the code took quit…

  • Rejected

    I was rejected a few weeks ago, so I'm posting this now. The phone screen was nothing special, the classic payment question, and the interviewer was very helpful. Onsite: Hiring manager round. Nothing much, it's basically just BQ, and they all came out about the same. System design, the classic food review. The interviewer barely discussed anything with me the whole time. Basically I was the one…

  • An AI-Coding Onsite Round, Rejected After System DesignDoorDash · Software Engineer · Onsite · Sep 2026
    Rejected

    Screen interview: Code Craft: Dasher Pay. Didn't feel too hard — just make sure to talk through your approach with the interviewer before you start coding. It's a high-frequency question from the forum. Onsite Round 1: Debugging: Round Robin Load Balancer. Also a forum question. Onsite Round 2: AI Coding. They had me set up a microservice locally on my own machine, specifically to handle abnormal…

  • Rejected

    Phone screen: it was the classic bootstrap question, same as most of the interview reports out there. The hard part of the question is adding the HTTP response status handling on top. I strongly recommend the public EngineBogie write-up of this one ("Implement a BootstrapService to aggregate data from multiple services"). Onsite: Debug: the question was the same round robin one. It's actually not…

  • Five Onsite Rounds Including a Live AI Coding ChallengeDoorDash · Software Engineer · Onsite · Sep 2026

    Round 1 — Code Craft: Bootstrap / Aggregator API Problem You are tasked with implementing an API that exposes the required information and is as resilient to failures as possible in order to ensure a good user experience. Implement a Bootstrap API that takes a user_id, pulls information from three existing upstream services — Consumer, Payment, and Address — and combines it into a single response…

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