Pipeline Architectureonsite pipeline architecture· L52026
Design a DataBricks platform from the ground up; write YAML configuration for each data source and explain the pipeline orchestration architecture including the semantic layer.
Senior DE onsite at DoorDash, 2026. The system design question was to design a DataBricks-like platform. The interviewer specifically wanted YAML code written for each data source, testing infrastructure-as-code knowledge for data pipelines. The candidate wrote a pseudo-script and answered follow-up questions on each architectural component. The candidate struggled with the semantic layer component. The question contained 6–7 lines of requirements. Source: r/dataengineering post id 1qv0iyl, February 2026, 2158-char post by a Senior DE candidate at DoorDash.
SQLphone screen sql· L52026
Sum(case when attribute is true)
Data Modelingonsite data modeling· L52026
Design a data model for a fitness tracking app; the interviewer also required drawing advanced trend visualizations, indicating DoorDash data engineers work with visualization layers.
Senior DE onsite at DoorDash, 2026. The data modeling round asked the candidate to design a fitness tracking app schema. Unusually, the interviewer also required the candidate to draw visualizations—specifically an advanced trend graph—not just a schema. The candidate noted: "Never in my past 8 years of work experience I had to do any visualizations but looks like DE in DoorDash work on visualizations as well." No specific schema constraints were provided upfront; the candidate was expected to define entities, facts, and metrics independently.
Pipeline Architectureonsite pipeline architecture· L52025
Design a URL shortener; the interviewer expected the candidate to approach it from a data engineering perspective, not standard SWE system design.
Senior DE onsite at DoorDash, 2025. The system design round asked the candidate to design a URL shortener. The candidate noted this is a classic SWE system design question and was unsure how to frame it from a DE perspective. A DE-angle approach would involve: key-value store design for short→long URL mapping, high-throughput write ingestion for new short links, read-optimised lookup, and analytics tables tracking redirect click events. Source: comment in r/dataengineering "Senior Data Engineer Experience (2025)", post id 1q034du, December 2025.
SQLonsite sql· L52025
Write a query that calculates the bad experience rate for new users who signed up in June 2022 during their first 14 days on the platform
Tables: orders(order_id, customer_id, trip_id, status, order_timestamp), trips(dasher_id, trip_id, estimated_delivery_timestamp, actual_delivery_timestamp), customers(customer_id, signup_timestamp). Join orders to trips and customers, filter to users who signed up in June 2022, restrict to orders within 14 days of signup, compute ratio of bad-status orders to total orders.
Data Modelingonsite data modeling· L52025
Design tables to support a complex metric defined by the interviewer; the metric definition itself was difficult to understand before designing the schema.
Senior DE onsite at DoorDash, 2025. The data modeling round presented the candidate with a complex, poorly-defined metric and asked them to design supporting tables. The candidate noted: "They gave me some weird metric that I needed to build tables for. It was difficult to even understand what the metric was. I was a bit lost on it." This tests the ability to clarify ambiguous requirements and translate a vague business metric into a fact/dimension schema. Source: comment in r/dataengineering "Senior Data Engineer Experience (2025)", post id 1q034du, December 2025.
Pipeline Architectureonsite pipeline architecture· L62025
How would you add and backfill a new column to a billion-row table in production without causing downtime? Describe your approach step by step.
This tests schema evolution and operational engineering. Key steps: (1) ALTER TABLE to add nullable column with a default value — online DDL supported by most modern databases, no downtime, (2) batch backfill: UPDATE the column in small chunks (e.g. WHERE id BETWEEN x AND x+10000) with COMMIT between batches to avoid long-running transactions and locking, (3) add NOT NULL constraint after backfill is complete. Follow-ups: how to handle concurrent writes during backfill (use atomic CAS or make the column nullable first), rollback strategy, monitoring the backfill progress, using…
SQLonsite sql· L52025
Write a SQL query to find orders that were delivered later than their promised delivery time.
Schema: orders(order_id, customer_id, order_time, promised_delivery_time, actual_delivery_time). Task: select orders where actual_delivery_time > promised_delivery_time. Extended follow-up: compute the average late delivery time by restaurant or city, and percentage of late orders per day. Requires timestamp comparison, WHERE clause filtering, and potentially GROUP BY for follow-up analytics. From DoorDash Data Engineer interview on InterviewQuery.
Pipeline Architectureonsite pipeline architecture· L62024
How would you identify which database tables are being queried when a black-box internal application retrieves data for a specific user, given no access to the application source code?
This tests database observability and debugging skills. Approaches: (1) enable query logging / slow query log in the database, filter by session or user identifier, (2) use network packet capture (e.g. tcpdump or Wireshark) on the database port to inspect SQL queries, (3) if using a proxy or connection pooler (PgBouncer, ProxySQL), inspect query logs there, (4) use database-native tools: pg_stat_activity (PostgreSQL), SHOW PROCESSLIST (MySQL), Query Store (SQL Server). Follow-ups: how to filter to a specific user, handling parameterized queries. From DoorDash DE interview testing data…
