DoorDash interview questions

Showing 211–240 of 241 results

• Model schema and query new-market readinessMedium

  This question evaluates SQL-based data manipulation skills including time-series aggregation, cohort analysis, percentile/median calculations, joins between event and reference tables, and deriving operational KPIs for new-market readiness; it targets the Data Manipulation (SQL/Python) domain for a Data Scientist role.

  DoorDashData ScientistData Manipulation (SQL/Python)Onsite
• Own Production Incidents and Validate Large Pipeline MigrationsMedium

  Plan scalable migration checks, detect silent zero-output failures, and define incident recovery and monitoring ownership without a separate QA team.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Paying Dashers Reliably When the Payment Service Is UnavailableMedium

  A system design follow-up about paying delivery drivers when the downstream payment service is unavailable. It tests reasoning about timeouts with unknown outcomes, durable records of amounts owed, retries that must never double-pay, recovery after long outages, reconciliation, and what drivers and operators see while payouts are delayed.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Pooled Rideshare Earnings With Concurrent Rides and SurgeMedium

  Compute a pooled-ride driver's total earnings where each minute's pay scales with how many riders are currently in the car, and minutes inside non-overlapping surge windows pay double, testing sweep-line interval counting with exact half-open boundaries and 64-bit totals.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Present Your Proudest ProjectHard

  Tests your ability to articulate a high-impact technical project end-to-end, highlighting architectural ownership, difficult trade-offs, rollout execution, and quantifiable business impact.

  DoorDashSoftware EngineerBehavioral & LeadershipOnsite
• Prioritize projects and manage tight deadlinesHard

  This question evaluates prioritization, scope definition, stakeholder leadership, resource and risk management competencies for a Data Scientist in the Behavioral & Leadership category.

  DoorDashData ScientistBehavioral & LeadershipOnsite
• Process Orders Through a DAG with Completion and Timeout RefundsMedium

  Design order DAG execution with prerequisite state, atomic completion-versus-timeout decisions, idempotent refunds, durable recovery, and accurate status queries.

  DoorDashSoftware EngineerSystem DesignOnsite
• Project Deep Dive: Technology Choices, Trade-offs, Hindsight and Working with PMsMedium

  A DoorDash project deep dive and hiring manager conversation about a project you are proud of. It probes why you chose your technologies, which alternatives and trade-offs you weighed, the challenges you faced, what you would change, and how you worked with other teams and product managers.

  DoorDashSoftware EngineerBehavioral & LeadershipOnsite
• Protect SLA and Choose StorageMedium

  This question evaluates system design competencies in service reliability and storage architecture, focusing on SLA protection during upstream latency spikes and trade-offs in choosing in-memory caches (e.g., Redis), relational databases, or alternative storage for order and payment state.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Recover a Project That Has Gone Off TrackMedium

  This question assesses a candidate's ability to demonstrate leadership, analytical rigor, and project management skills when facing technical or execution failures.

  DoorDashSoftware EngineerBehavioral & LeadershipTechnical Screen
• Reduce Cold-Food Incidents with Metrics and an Insulated-Bag ExperimentEasy

  Design an A/B test and metric framework to evaluate whether equipping couriers with insulated bags reduces cold-food complaints while guarding marketplace efficiency.

  DoorDashData ScientistAnalytics & ExperimentationTechnical Screen
• Resolve Conflicts and Deliver Results Under PressureMedium

  DoorDash data scientist behavioral prompt on cross-functional disagreement, delivering under limited data or resources, receiving critical feedback, STAR-L storytelling, measurable results, and reflection.

  DoorDashData ScientistBehavioral & LeadershipOnsite
• Resolve Disagreement, Use Feedback, and Grow Engineering JudgmentMedium

  Discuss technical disagreement, constructive feedback, concrete growth practices, and how varied perspectives contribute to accountable team decisions.

  DoorDashSoftware EngineerBehavioral & LeadershipOnsite
• Resolve a Workplace ConflictHard

  Tests your ability to navigate high-stakes technical or cross-functional disagreements constructively using data, empathy, and active listening to achieve a durable outcome.

  DoorDashSoftware EngineerBehavioral & LeadershipOnsite
• Review Constant-Time Random Courier Selection and RemovalMedium

  Maintain dense map indices for uniform random selection and swap-with-last removal, including empty states, concurrency, and complexity checks.

  DoorDashSoftware EngineerSoftware Engineering FundamentalsOnsite
• Scale the cache to a distributed systemHard

  This question evaluates a candidate's ability to design and scale a distributed caching system, testing competencies in sharding and consistent hashing, replication and consistency models, request routing, failure detection, cache invalidation, capacity planning, and operational trade-offs for high-throughput, low-latency workloads.

  DoorDashSoftware EngineerSystem DesignTechnical Screen
• Select the best dasher for an orderMedium

  This problem evaluates algorithmic selection and geometric computation skills—filtering eligible dashers, calculating Euclidean distances, and applying deterministic tie-breaking—within the Coding & Algorithms domain at an implementation-level algorithmic abstraction.

  DoorDashSoftware EngineerCoding & AlgorithmsOnline Assessment
• Should DoorDash add bicycle dashers?Medium

  This problem tests the ability to design an A/B test for a marketplace intervention, identify success metrics, and account for potential cannibalization and external factors in urban delivery.

  DoorDashData ScientistAnalytics & ExperimentationTechnical Screen
• Simulate Constant-Time Random Courier SelectionMedium

  Design a courier pool data structure that supports adding, removing, and uniformly selecting random courier IDs in average O(1) time.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Solve BFS grid delivery routingMedium

  This question evaluates proficiency in grid-based graph traversal and shortest-path computation, along with complexity analysis and handling dynamic changes, assessing skills in algorithm design, graph algorithms, and data structures within the Coding & Algorithms domain.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Solve multi-part SQL with sliding windowsMedium

  This question evaluates a candidate's practical SQL data-manipulation skills, including time-based rolling aggregates, distinct counts and deduplication, multi-table joins, revenue aggregation and ranking, and session/streak computations within the Data Manipulation (SQL/Python) domain.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Solve string match and DashMart BFSMedium

  This question evaluates proficiency with string algorithms and pattern matching, assessing understanding of substring search complexity and algorithm selection within the algorithms and data structures domain, and is commonly asked to verify efficient low-level text-search implementation and time/space trade-off reasoning at a practical implementation level. The "Dash Mart" grid BFS evaluates graph traversal, state-space modeling, and shortest-path reasoning for multi-target collection tasks within graph algorithms and path-finding, and is commonly asked to measure competence in BFS-based search, state encoding and optimization strategies with a focus on practical algorithmic application and performance considerations.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Solve tree distance and design file systemMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Solve tree distance and design file system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite
• Track the First Unique Restaurant in a StreamMedium

  This problem tests your ability to maintain stream uniqueness using a combination of a hash map and a doubly linked list to ensure constant-time operations.

  DoorDashSoftware EngineerCoding & AlgorithmsOnsite
• Validate a Shopping CartMedium

  Implement a shopping cart validation function that checks item availability, consolidates duplicate entries, verifies inventory levels, and ensures order minimums are satisfied.

  DoorDashSoftware EngineerCoding & AlgorithmsTechnical Screen
• Walk Through an ETL ProjectHard

  This question evaluates an analytics engineer's end-to-end ETL and data engineering competencies, including data ingestion, transformations, data modeling, orchestration, testing, monitoring, backfills, and handling failures or schema changes.

  DoorDashAnalytics EngineerBehavioral & LeadershipTechnical Screen
• Walk Through an ML ProjectEasy

  This question assesses the candidate's ability to communicate a full-lifecycle machine learning project, focusing on problem scoping, technical execution, and business impact.

  DoorDashMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Write SQL for deliveries analyticsMedium

  This question evaluates SQL data manipulation and analytics skills—specifically aggregation, conditional counting, time-window filtering, joins, and handling of nulls and zero-count periods.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL for percent and window changesMedium

  This question evaluates proficiency with advanced SQL techniques—specifically the use of CTEs and multiple window functions, row-level versus aggregated conditionals, time-windowed aggregations, generation of unit-day series including zero-order days, and correct handling of missing exposure labels for percent and change calculations.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen
• Write SQL to backtest refund policyMedium

  This question evaluates advanced SQL data-manipulation and analytics competency, including time-based lateness calculations, window functions (PERCENT_RANK and rolling/partitioned windows), CTEs and joins, aggregation, and monetary-field handling for a policy-simulation scenario.

  DoorDashData ScientistData Manipulation (SQL/Python)Technical Screen