How difficult are DoorDash Data Manipulation (SQL/Python) interview questions?

DoorDash data manipulation questions are often medium to high in difficulty because they test practical problem solving under time pressure rather than purely theoretical knowledge. Interviewers evaluate your ability to wrangle messy, production-like datasets, write correct and efficient SQL or pandas code, and reason about edge cases, performance, and tradeoffs. Expect multi-step problems that combine joins, aggregations, windowing, and time-based manipulations, or Python tasks that require clean data pipelines and defensible assumptions. Success depends on clear thinking, communicating assumptions, and producing working, well-structured queries or scripts within the interview time limits.

Where in the DoorDash interview process do Data Manipulation (SQL/Python) questions appear, and what formats are used?

Data manipulation questions commonly appear in the technical phone screen and in one or more onsite/virtual loop rounds for analytics, data science, and data engineering roles. Formats include live coding in a SQL editor or CoderPad-style environment, short timed SQL quizzes during screens, and take-home assignments or timed notebooks for deeper analysis. Onsite rounds typically present marketplace-style scenarios that require joining event, order, and user tables, cohort calculations, or Python-based cleaning and analysis. Interviewers expect you to articulate approach, validate intermediate results, and connect outputs back to business metrics.

How should I structure my interview preparation timeline for Data Manipulation (SQL/Python)?

Over a 4–6 week timeline, prioritize fundamentals first, then simulate interview conditions. Start with daily drills on SQL: joins, groupings, window functions, CTEs, and handling NULLs. Parallel your Python work on pandas: merges, groupby, time-series resampling, and vectorized transformations. Midway through practice timed problems and explain your steps aloud to build communication skills. In the final two weeks, do mixed mock interviews that combine SQL and Python tasks, review common marketplace scenarios, and rehearse concise business-oriented explanations. Finish by polishing code readability and running end-to-end examples on sample datasets.

What are the key subtopics within Data Manipulation (SQL/Python) that I should master for DoorDash interviews?

Key SQL subtopics include inner/outer joins, aggregations and HAVING, window functions for running totals and lead/lag, CTEs/subqueries, time-based grouping, cohort and retention calculations, NULL handling, and basic performance awareness (indexes, avoiding large cross joins). For Python, focus on pandas DataFrame manipulations: merges, groupby/agg, pivoting, datetime conversions, vectorized operations, memory-aware practices, and writing clear transformation pipelines. Also gain familiarity with cleaning steps, validating intermediate results, and producing minimal, well-documented code that can be explained to non-technical stakeholders.

What standout tips and common pitfalls should I keep in mind for Data Manipulation (SQL/Python) interviews at DoorDash?

Standout tips include clarifying the question and assumptions before coding, walking through schema choices, writing small test queries or sample outputs, and vocalizing tradeoffs between correctness and performance. Keep answers business-oriented by tying results to metrics. Common pitfalls are ignoring NULLs and timezone issues, mishandling duplicates, overcomplicating queries instead of using CTEs for clarity, and failing to test edge cases. In Python, avoid slow row-wise operations when vectorized alternatives exist and ensure your code is readable and modular so interviewers can quickly follow and probe your logic.

Doordash Data Manipulation (SQL/Python) Interview Questions

Preparing for DoorDash Data Manipulation (SQL/Python) interview questions means focusing on real-world, marketplace-style problems where clean, performant data work matters as much as the final number. Interviewers typically evaluate accuracy, query efficiency, clarity of assumptions, and product intuition—expect questions built around orders, deliveries, time-based cohorts, and windowed aggregations rather than toy datasets. ([davidfosterhq.medium.com](https://davidfosterhq.medium.com/doordash-data-scientist-interview-questions-guide-2026-211cdf8cd1a1?utm_source=openai)) You should expect a technical screen that often includes live SQL coding and one or two Python/pandas problems, plus product or case-style discussions that probe metric design and trade-offs. For effective interview preparation, practice joins, CTEs, window functions, and datetime logic, and write concise pandas transformations; timebox your work, verbalize assumptions and edge cases, and rehearse explaining results to non-technical stakeholders. Treat sample DoorDash scenarios (ETAs, driver efficiency, cancellations, and cohort analyses) as practice ground to combine technical correctness with clear business recommendations. ([datainterview.com](https://www.datainterview.com/blog/doordash-data-scientist-interview?utm_source=openai))

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25 Questions 1 Company

ChrisSenior SWE, LinkedIn

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@sleepy33

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@nuggetlord

"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."

CarlosFull Stack, Shopify

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@boba.tea.vibes

"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."

AndySWE-II, Google

"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."

@couchpotato99

"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."

ShrutiData Engineer, Salesforce

"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."

@midnightramen

"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."

BiancaFrontend Eng, Figma

"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."

@toa

"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."

ChrisSenior SWE, LinkedIn

"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."

@sleepy33

"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."

JakeSenior ML Engineer, Lyft

"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."

@nuggetlord

CarlosFull Stack, Shopify

"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."

@boba.tea.vibes

"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."

AndySWE-II, Google

"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."

@couchpotato99

"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."

ShrutiData Engineer, Salesforce

"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."

@midnightramen

"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."

BiancaFrontend Eng, Figma

"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."

@toa

"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."

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Doordash Data Manipulation (SQL/Python) Interview Questions

Analyze Spending Patterns and Restaurant Performance Using SQL/Python

Write SQL for percent and window changes

Write SQL for late-delivery metrics by window

Generate Weekly Revenue and Engagement Summary with Pandas

Refactor SQL into an aggregated report

Write SQL for cuisine median delivery times

Measure Late Deliveries and Identify Top Delayed Restaurants

Write complex SQL on DoorDash data

Find orders from bottom-quartile revenue restaurants

Compute rolling cold-delivery rates with windows

Model schema and query new-market readiness

Write SQL for cold-complaint diagnostics with LAG/QUALIFY

Write SQL to backtest refund policy

Solve multi-part SQL with sliding windows

Compute dasher payout from API data

Implement a gig worker payout calculator

Compute dasher pay from deliveries

Compute courier pay with peak-hour rules

Calculate Late Delivery Percentage and Top Customers

Analyze Monthly Customer and Restaurant Spend Data

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