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Disney interview notes, page 4 of 4

Large Enterprises

Most of these Disney write-ups come from Software Engineers (20), then Product Managers (9) and Data Scientists (5). The most reported rounds are the technical phone screen (15), the system design round (7), the product sense round (5) and the hiring manager screen (4).

38 interview notes · updated Jul 2026

Disney·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Disney frontend system design interview, two meaty parts back to back. The shopping cart section felt manageable but the feature flag question caught me flat-footed on the consistency guarantees side. Solid experience overall, left feeling like I'd done maybe 70% of what I was capable of.
  • Design the shopping cart experience for a large e-commerce app, covering anonymous vs. logged-in carts, multi-tab behavior, offline support, real-time price and stock changes, data modeling, client state management, sync strategy with the backend, performance, and observability.
  • Design a feature flag and remote config system for gating UI features, including targeting by user segment or percentage rollout, delivery to clients, consistency across sessions and devices, safety defaults, and hooks for A/B testing.

“This was the bulk of the first half and I actually felt decent about it.”

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Disney·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Disney ML Engineer interview with a computational geometry/simulation problem that honestly felt more like a physics puzzle than anything I'd call machine learning. One question, pretty deep, left me second-guessing my approach the whole way through.
  • You have N points arranged in a cycle in 2D space. Each point continuously chases the next point in the cycle at constant speed. Determine whether all points ever converge to the same location simultaneously, and if so, output the meeting time and coordinates.

“I stared at this for a good 30 seconds before saying anything.”

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Disney·Machine Learning Engineer·Take-home Assignment

Jul 2026
Disney ML Engineer take-home that was basically a spam classifier build-out in a Jupyter notebook. Pretty self-contained but there were a few gotchas around class imbalance and leakage that I think they were specifically watching for.
  • Build a spam detection classifier using Naive Bayes on a provided text dataset. Handle preprocessing, feature extraction, model training, and evaluation with F1 score, then run predictions on a few test examples.

“The F1 part tripped me up a bit.”

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Disney·Product Manager·Onsite - Product Sense / Strategy

Jul 2026
Got a product sense question for a PM role at Disney, just the one question from what I can tell. Short but made me think more than I expected.
  • How would you improve TED?

“I went straight to the content discovery angle because that felt obvious, but halfway through I realized I hadn't actually defined what 'improve' meant or for whom.”

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Disney·Software Engineer·Technical Phone Screen

Jul 2026
Disney SWE interview that was pretty much a BFS problem dressed up with a lot of follow-up questions. More depth required than I expected for what looked like a straightforward grid traversal problem.
  • Implement a function to find the shortest path length in an unweighted 2D grid from the top-left to the bottom-right cell, where '0' is open and '1' is a wall, movement is four-directional, and the function returns -1 if no path exists.
  • Why is BFS the right algorithm here, and what are the time and space complexities?
  • What edge cases would you consider for this problem, including single-cell grids, blocked start or end, and multiple valid shortest paths?
  • How would you extend this solution to support diagonal movement, and what specifically needs to change in the implementation?

“I jumped straight to BFS which was correct, but I fumbled explaining *why* BFS and not DFS for a second.”

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Disney·Data Scientist·Take-home Assignment

Jul 2026
Disney/Hulu data science take-home that was basically a gauntlet of SQL funnel questions on a streaming product schema. Five sub-parts, each harder than the last, and the bonus rolling-window question nearly broke me. Felt more like an engineering screen than a DS interview.
  • Build a daily impression-to-click-to-signup-to-subscription funnel for each day in a date range, with time-bounded attribution windows at each stage (click within 1 day of impression, signup within 3 days of click, subscription within 7 days of signup). Output daily counts and stage-to-stage conversion rates.
  • For users who signed up in August 2025, compute the median time in minutes from signup to first watch_start, broken out by signup date and acquisition channel (organic vs. paid based on whether campaign_id is null).
  • Identify suspicious ad campaigns in the last 7 days where the click-through rate exceeds 80%, there are at least 100 impressions, and the number of distinct users is 5 or fewer. Return campaign details including CTR and distinct user count.
  • Deduplicate events by collapsing identical (user_id, event_type, show_id) combinations that occur within a 5-second window, keeping only the earliest timestamp. Produce a clean deduplicated events CTE.
  • Compute a 28-day rolling count of unique viewers per show using watch_start events, for each day across August and September 2025. Return the top 3 shows by this rolling metric on a specific target date, breaking ties by most recent daily unique viewers.

“This one took me longer than it should have.”

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Disney·Software Engineer·Technical Phone Screen

Jul 2026
Had a technical screen for a Data Engineer role at Disney where they threw a classic recursion/backtracking problem at me. Pretty standard coding round, nothing too wild, but the problem had some edge cases worth thinking about.
  • Given an integer n, write a function that generates all valid combinations of n pairs of parentheses.

“I knew this was a backtracking problem pretty quickly, the trick is tracking how many open and close brackets you've placed so far and only adding a close bracket when it won't break validity.”

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Disney·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at Disney for a data engineering role. The whole session was basically one giant pipeline design question with a bunch of follow-ups that got progressively harder. Left feeling okay about the high-level stuff but shaky on the correctness details.
  • Design an end-to-end clickstream ingestion and aggregation pipeline that handles roughly a billion events per day, covering everything from the client SDK through to the serving layer where analysts query daily aggregates.
  • A user's device goes offline and syncs telemetry hours or even a day later. How do you make sure the daily aggregate stays accurate? Walk through how you'd handle late-arriving events, including what happens after the aggregate has already been finalized.
  • Network retries cause duplicate events to show up in the pipeline. What's your strategy for exactly-once processing? Compare idempotent sink writes versus transactional two-phase-commit sinks, and explain where dedup state lives and how you keep it from growing forever.
  • Traffic spikes 10x during a live event. How do you detect that the pipeline is falling behind, and what's your autoscaling and back-pressure strategy to catch up without dropping data?
  • A bug in the aggregation logic shipped yesterday. How do you recompute corrected aggregates from the raw event store without disrupting live ingestion?

“This is the kind of question where you can talk for 45 minutes and still feel like you missed something.”

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