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Confluent interview notes, page 1 of 4

Large Enterprises

Most of these Confluent write-ups come from Software Engineers (35), then Data Scientists (2) and Product Managers (1). The most reported rounds are the technical phone screen (24), the system design round (11), the onsite coding round (2) and the behavioral / leadership round (1).

38 interview notes · updated Jul 2026

Confluent·Software Engineer·Technical Phone Screen

Apr 2026
Confluent software engineering interview that went deeper than I expected. Started with a data structure design problem and then kept piling on follow-ups until I was basically doing a distributed systems lecture in my head.
  • Design a random queue: a FIFO queue where dequeue returns a random element from the current contents instead of the front element.
  • How would you determine whether two random queues are equal?
  • What multithreading issues come up with a random queue, particularly around concurrent enqueue and dequeue operations and maintaining consistency during an equality check?
  • If the queue is stored using variable-length run-length encoding, how would you compare two such encoded queues for equality?

“My first instinct was to reach for an array and swap the chosen element to the front before removing it, which works but I fumbled explaining the time complexity clearly.”

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Confluent·Software Engineer·Technical Phone Screen

Apr 2026
Confluent SWE interview with a data structure design problem that looks deceptively straightforward until you think about the INCREMENT operation. The key insight they're fishing for is the lazy global offset trick, and if you don't know it going in, you'll probably waste time re-heapifying everything.
  • Design a system to process three types of pod events: adding a pod with load 0, incrementing every existing pod's load by 1, and removing the pod with the smallest load while returning that load. Process a sequence of these events and return all outputs from the remove operations in order, with an efficient amortized solution.

“The add and remove parts are fine, just a min-heap.”

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Confluent·Data Scientist·Technical Phone Screen

Apr 2026
Confluent data scientist interview with a SQL-heavy technical screen focused on A/B testing analysis. The question was more involved than it looked at first glance, requiring you to think carefully about what 'completion' even means when the max step differs by variant.
  • Given a users table and a tutorial events table, write a SQL query that returns, for each tutorial variant, the percentage of signed-up users who completed the tutorial (reached the maximum step) within 7 days of signing up.

“The 7-day filter tripped me up more than I expected.”

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Confluent·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at Confluent for a software engineer role. The whole thing was focused on designing a disposable email service at scale, which sounds simple enough until you're 20 minutes in and realizing you forgot to think about SMTP ingestion at all.
  • Design a disposable/temporary email service that can support 100 million active users.

“I started with address generation and inbox reads, which felt natural, but the interviewer kept nudging me toward the write path.”

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Confluent·Software Engineer·Technical Phone Screen

Apr 2026
Interviewed for a software engineering role at Confluent and got a Trie problem I hadn't seen before. The interviewer was hard to communicate with and I didn't finish in time. Rejection came the next day.
  • Given a Function class with a name and a list of argument types, implement a register() method and a findMatches() method. findMatches() should return all registered functions whose argument type list starts with the given prefix sequence of types.

“I knew a Trie was the right structure pretty quickly but couldn't get the implementation clean under pressure.”

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Confluent·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Confluent software engineer interview focused heavily on the problem-solving process itself, not just getting to a correct answer. The whole thing felt like a live demonstration of how you think, which is either refreshing or exhausting depending on your prep.
  • Walk through your full process for tackling a complex, ambiguous coding problem: how do you clarify requirements, identify constraints, propose multiple approaches, choose the right data structures and algorithms, analyze complexity, handle edge cases, and evolve from a brute-force solution to something clean and optimized?

“This is basically asking you to narrate your entire engineering brain out loud.”

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Confluent·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Confluent software engineering interview with three back-to-back technical problems. The questions leaned heavily on systems thinking and edge cases rather than pure leetcode grinding, which I wasn't fully prepared for.
  • Given a registry of function definitions where each function has required, optional, and variadic parameters, determine whether a given ordered list of argument types matches at least one registered function. Assume exact type matching with no coercions.
  • Implement a utility to print the last N lines of a file that may be too large to fit in memory. You can only use a minimal file API with size(), read(k), and move(delta). Discuss the tradeoffs between fixed-buffer scanning and backward seeking, and stream output to stdout without materializing the full answer first.
  • Design a data structure that works like a queue for insertions but returns a uniformly random element on removal instead of the oldest. Then discuss how you'd check equality between two such queues, what breaks in a multithreaded context, and how equality checking changes if elements are stored with variable-length run-length encoding.

“This tripped me up more than I expected.”

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Confluent·Software Engineer·Technical Phone Screen

May 2026
Confluent SWE interview with a ranking/voting algorithm problem that looks deceptively clean on the surface but has enough edge cases to keep you busy for a while.
  • You're given preference rankings from n users over m songs. Define a 'beats' relation where song x beats song y if more than half the users prefer x over y, or exactly half prefer x and x has a smaller ID. Then define popularity by how many songs each song beats, breaking ties by song ID. Return all songs sorted from most to least popular.

“This one took me a minute to parse.”

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Confluent·Data Scientist·Technical Phone Screen

May 2026
A product analytics question for a Data Scientist role at Confluent, focused entirely on experiment design around a single onboarding tutorial step change. Pretty deep for what felt like a scoped problem at first glance.
  • A product team changed one specific step in Confluent's user onboarding tutorial. What primary and secondary metrics would you track that are closely tied to that particular step?
  • Should you randomize at the user level or the account level for this experiment, and what covariates would you check to confirm the groups are balanced?
  • Which statistical test would you use for this experiment, how would you calculate the required sample size and estimated runtime, and what would you do differently if the available sample size is very small?

“My instinct was to go broad and list generic retention metrics, which was the wrong move.”

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Confluent·Software Engineer·Technical Phone Screen

May 2026
Confluent SWE interview with a Sudoku validation problem. Pretty standard coding round, nothing too wild, but the edge cases kept me second-guessing myself.
  • Given a 9x9 Sudoku board that may be fully or only partially filled in, write a function to determine whether the current state is valid.

“I jumped straight to the fully-completed case and started coding row/column checks before they clarified it could be partial.”

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