Nextdoor Software Engineer Interview Questions

Nextdoor·Software Engineer·Technical Phone Screen

Jul 2026

Coding screen at Nextdoor for a software engineer role. One problem, interval merging but with a twist on the input format that tripped me up more than I expected.

Given a list of time intervals expressed as day-of-week plus 24-hour clock timestamps (e.g. 'Mon 09:00'), write a function that merges all overlapping intervals and returns them in chronological order.

“The merging logic itself is pretty standard but I lost a few minutes fumbling with the parsing step.”

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Nextdoor·Software Engineer·Technical Phone Screen

Jul 2026

Nextdoor software engineer interview with a meaty coding problem around version string comparison. The question had a lot of layers and I didn't fully anticipate how deep they'd want to go on the pre-release parsing piece.

Implement a compareVersions(a, b) function that compares two version strings with dot-separated numeric components, treating missing trailing parts as zero and ignoring leading zeros. Then extend it to handle optional pre-release tags like -alpha or -rc.1 (which sort before the release), compare pre-release tokens numerically or lexicographically depending on type, strip build metadata after '+', and return -1, 0, or 1. Also walk through complexity and describe edge case test coverage.

“Started fine with the basic numeric comparison, split on dots, parseInt to drop leading zeros, pad shorter arrays with zeros.”

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Nextdoor·Software Engineer·Technical Phone Screen

Jun 2026

Coding round for a software engineer role at Nextdoor. One problem, feed grouping, which sounds straightforward until you actually sit down and think through the edge cases.

You're given a list of feed items, each with an id, type (Normal, Video, or Photo), and a score. Group every 3 consecutive Photos into a PhotoGroup (keeping their original order), where the group's score is the max score among the three. Then sort all items, including ungrouped ones, by score descending.

“The grouping part clicked pretty fast but I fumbled on the score assignment for a bit.”

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Nextdoor·Software Engineer·Technical Phone Screen

Jun 2026

SQL-heavy technical screen for a data engineering role at Nextdoor. The whole thing was built around a photo-sharing app schema and escalated pretty fast from basic counts to populating aggregate tables and computing retention cohorts. Not a vibe check at all, just raw SQL for like an hour.

Given a users table, write a query to count the total number of users.
Using the users table, find the calendar month with the highest number of new user signups.
Find the user with the largest follower count using the follows table.
Write SQL to populate a set of daily aggregate tables from raw event, user, photo, and follows data. The dashboard needs new users, DAU, new photos, new photos per DAU, total follow edges, and follower bucket distributions, all with 7-day comparisons and timezone conversion to America/Los_Angeles.
Extend your SQL to compute week-1 retention: for users who signed up on day D, what share had at least one event between day D+7 and D+13, using the reporting timezone?

“Warmup question, COUNT(*) on users.”

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Nextdoor·Software Engineer·Technical Phone Screen

Jun 2026

Nextdoor software engineering interview that was basically a full ML pipeline exercise in Colab. Dense question covering a lot of ground at once, felt more like a take-home crammed into a live session.

Design and implement an end-to-end classification workflow on a tabular dataset in Google Colab. Cover data loading, EDA, preprocessing (missing values, scaling, encoding), train/validation split, model selection from baseline to stronger models, hyperparameter tuning, and evaluation with justified metrics. Also address class imbalance, data leakage prevention, cross-validation, and confidence interval reporting.

“This was a lot to hold in your head at once.”

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Nextdoor·Software Engineer·Technical Phone Screen

May 2026

Nextdoor coding round, basically a Merge Intervals variant but with weekly calendar strings instead of plain numbers. The twist kept it interesting but also added a lot of places to mess up.

Given a list of time intervals expressed as string pairs in the format 'DAY HH:MM' (where DAY is Mon through Sun), merge all overlapping or adjacent intervals and return the result sorted chronologically. Intervals where the end comes before the start should be treated as wrapping past Sunday into the following Monday, so split them into two sub-intervals before merging.

“The core merge logic wasn't the hard part, I've done LC 56 before.”

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Nextdoor·Software Engineer·Technical Phone Screen

May 2026

Nextdoor analytics engineering interview, SQL-heavy round focused on building idempotent ETL pipelines on top of rollup tables. The question was a continuation of an earlier dashboard design problem, so if you haven't thought through the schema beforehand you're already behind.

Given raw tables for users, sessions, photos, likes, comments, and follows, write idempotent SQL ETL queries that compute and upsert KPIs (user growth, DAU, photo activity, and follower distribution including median and p90) into rollup tables for a target date parameter.

“This one took me a while to even parse.”

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Nextdoor·Software Engineer·Onsite - System Design / Architecture

May 2026

Nextdoor software engineer interview that leaned heavily on distributed systems depth. Three meaty questions back to back, all requiring you to draw from real production experience rather than textbook answers.

Explain the CAP theorem and walk through a real system you designed: which two properties did you prioritize during a network partition, what trade-offs came with that choice, and why did you make it?
Describe your hands-on experience building and running distributed systems: what architectures did you use, what were the hardest problems you hit around consistency, fault tolerance, scaling, or observability, and how did you actually solve them?
Talk through your experience with multithreaded programming: what synchronization primitives have you used, how did you prevent deadlocks and data races, how did you debug concurrency bugs, and what did you do to improve performance?

“The 'explain CAP' part is easy enough but they immediately pushed into a real example, which is where it gets uncomfortable.”

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Nextdoor·Software Engineer·Onsite - System Design / Architecture

May 2026

Data engineering interview at Nextdoor centered entirely on one massive analytical modeling question. It was a multi-part beast covering table design, ETL strategy, retention, rollups, and timezone handling. Felt more like a take-home that got crammed into a live session.

Design the derived table schema that powers a fast daily KPI dashboard for a large photo-sharing app. Cover grain, keys, what gets precomputed versus computed at query time, and how you'd handle partitioning, clustering, and incremental loads.
Walk through the daily ETL process to populate these KPI tables from the raw source tables. High-level steps are fine, SQL or pseudocode is acceptable.
How would you add week-1 retention to this dashboard? For each signup cohort date, what fraction of users are active exactly one week later? What table changes are needed and how do you compute it efficiently?
The dashboard needs to support weekly rollups instead of daily. What changes in the table design and computation, particularly around non-additive metrics, partial weeks, and how 'change vs 7 days ago' translates to a weekly grain?
A New York office wants the same metrics reported in Eastern time instead of Pacific. What needs to change across timestamp handling, ETL windows, backfills, and how do you support both timezones without double-counting events at day boundaries?

“This is where I spent most of my energy and still felt like I was only halfway there.”

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Nextdoor·Software Engineer·Technical Phone Screen

May 2026

SQL round for a Data Engineer role at Nextdoor, built around a fake social app schema. Three questions, all relational, nothing crazy but the last one had a small wrinkle I didn't fully anticipate.

Using the users table, write a query to count the total number of users.
Which month saw the highest number of user signups? Return both the month and the count.
From the follows table, find the username of the user with the most followers. If there's a tie, return all tied users.

“Warmup question, basically just COUNT(*) FROM users.”

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