Standard opener but the 15-minute time box is real.
Choose a project that showcases cross-functional collaboration and adaptability in a fast-paced environment. Structure your answer using a clear narrative arc: context, challenge, actions, and results. Highlight how you navigated ambiguity and aligned stakeholders to deliver impact.
Pro tip: Quantify the impact of your project (e.g., reduced latency by X%, increased orders by Y%) and explicitly state how you adapted to changing requirements or feedback from cross-functional partners.
Briefly describe the project's goal, your role, and the team composition, emphasizing any cross-functional elements (e.g., working with product, design, operations).
Explain the specific problem or ambiguity you faced, such as unclear requirements, shifting priorities, or technical constraints, and why it mattered.
Walk through the steps you took to address the challenge, focusing on how you collaborated with others, made decisions, and adapted to changes.
Share the measurable results of your work, including business impact, technical improvements, and any positive feedback from stakeholders.
Conclude with key lessons learned, such as how you improved your cross-functional communication or became more adaptable in ambiguous situations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and scale, then break the problem into three independent subsystems: ratings aggregation, social posts with likes, and earnings settlement. For each, propose a high-level design with data models, storage choices, and trade-offs, emphasizing eventual consistency and idempotency where needed.
Pro tip: Highlight the importance of idempotent settlement and anti-manipulation in ratings, as these are common pitfalls in production systems at scale. Show awareness of cost and latency trade-offs by suggesting pre-aggregation for ratings and batch processing for settlements.
Ask about expected QPS, data volume, consistency needs, and latency requirements for each subsystem. Confirm whether ratings are per item or per restaurant, and how earnings are calculated (e.g., per delivery, tips, bonuses).
Propose a data model for ratings (user_id, item_id, rating, timestamp) and an aggregation strategy (e.g., stream processing with windowing or batch jobs). Discuss trade-offs between real-time and eventual consistency, and how to handle updates/deletes.
Outline a schema for posts (post_id, user_id, content, timestamp) and likes (user_id, post_id, timestamp). Suggest a storage solution (e.g., NoSQL for scalability) and discuss how to efficiently retrieve like counts and user feeds.
Define the settlement process: aggregate driver activities (deliveries, tips, adjustments) over a monthly period, compute earnings, and generate payouts. Emphasize idempotency, auditability, and handling late-arriving data.
Discuss scalability, fault tolerance, monitoring, and security. Mention how to handle spikes, data consistency across subsystems, and compliance with financial regulations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview actually lived.
Start by clarifying the system's scope and key access patterns (e.g., read-heavy vs write-heavy, real-time vs batch) to ground your modeling decisions. Then propose a high-level entity-relationship model, highlighting core entities, relationships, and trade-offs between normalization and denormalization for scalability. Finally, discuss how you would evolve the schema over time and handle data consistency across services.
Pro tip: Emphasize that data modeling at DoorDash is driven by access patterns and service boundaries—show you understand that a single monolithic schema is rarely optimal, and that denormalization and caching are often necessary for low-latency reads.
Ask questions to understand the scale, read/write ratios, latency requirements, and consistency needs for each entity. Identify the most frequent queries (e.g., fetching a restaurant's menu, user feed, delivery tracking).
List the main entities (FoodItem, Rating, Post, Like, Delivery, Settlement) and define their attributes and relationships (e.g., one-to-many, many-to-many). Sketch an ER diagram or describe it verbally.
For each entity, decide on the appropriate datastore (e.g., relational for transactions, NoSQL for scale, graph for social) and schema (normalized vs denormalized). Justify choices based on access patterns.
Discuss how to partition/shard data, handle hot spots, and ensure eventual consistency where needed. Mention caching strategies and materialized views for read-heavy entities.
Explain how the schema can evolve with new features (e.g., adding new rating types) and how services will interact (APIs, events). Highlight the importance of idempotency and data migration strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Relational for anything touching money, document store for posts, KV or Redis-style counters for likes.
Start by clarifying the system's components and their data access patterns, then map each to a storage technology based on read/write ratio, consistency needs, and scale. Justify choices with trade-offs and mention alternatives you considered.
Pro tip: Tie every choice to a concrete DoorDash scenario (e.g., order tracking needs low-latency reads, so use a cache like Redis in front of a durable store). This shows you understand the business, not just the tech.
Ask questions to identify the main parts of the system (e.g., user profiles, orders, real-time tracking, analytics) and their data characteristics (volume, velocity, access patterns, consistency needs).
For each component, propose a storage type (relational, key-value, document, wide-column, graph, search, cache, blob, time-series) based on its requirements, and explain why it fits.
Discuss the pros and cons of your choice versus alternatives, covering aspects like scalability, consistency, latency, cost, and operational complexity.
Explain how data flows between stores (e.g., caching, replication, ETL), and how you handle consistency, backup, and disaster recovery.
Recap your choices in a concise table or list, and ask if the interviewer wants to dive deeper into any specific area.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got here with maybe 10 minutes left so the answer was rushed.
Start by clarifying requirements and scale, then outline a high-level pipeline from data ingestion to pay statement generation. Dive into critical components like idempotency, retries, and auditability, explaining trade-offs and failure handling. Conclude with how you'd ensure correctness and compliance.
Pro tip: Emphasize idempotency at every stage and design for exactly-once semantics using unique transaction IDs and deduplication. Mention the importance of immutable audit logs and reconciliation to catch discrepancies early.
Ask about volume (number of drivers, deliveries), frequency (monthly), and regulatory requirements. Understand what data sources are involved (e.g., delivery records, tips, adjustments).
Outline stages: data collection, aggregation, calculation, payment initiation, and statement generation. Consider batch processing with distributed systems like Spark or workflow orchestrators like Airflow.
Use unique idempotency keys for each payment and deduplication logic. Implement retries with exponential backoff and dead-letter queues for failures, ensuring operations are safe to repeat.
Generate immutable records with detailed breakdowns (earnings, tips, adjustments, fees). Store in append-only ledger and provide versioned statements for audit trails.
Discuss consistency vs. availability, latency vs. throughput, and how to handle partial failures. Mention reconciliation processes and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.