Rubrik Software Engineer Interview Questions
15+ questions from real Rubrik Software Engineer interviews, reported by candidates.
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Hi all, I interviewd for Rubrik recently for SDE-2 role. My first round was System coding in this post will share the question and the solve which i gave. Interviewer was not supportive even my soluti
Hi everyone, I recently got this question in an OA (Rubrik): Given a binary string s consisting of only '0' and '1', find the minimum number of swaps (swap any two characters, not necessarily adjacent
**Context** Interview process for the Rubrik 2025 Grad role, conducted mid-September following recruiter outreach. **Online Assessment (1 Hour)** The assessment consisted of: * **DSA:** One question f
**Problem Statement** Design a system to implement two queues within a single fixed-size array of length $N$. External data structures are permitted for metadata management, but all actual data elemen
LeetCode #2249: Count Lattice Points Inside a Circle. Difficulty: Medium. Topics: Array, Hash Table, Math, Geometry, Enumeration. Asked at Rubrik in the last 6 months.
LeetCode #1242: Web Crawler Multithreaded. Difficulty: Medium. Topics: Depth-First Search, Breadth-First Search, Concurrency. Asked at Rubrik in the last 6 months.
LeetCode #2193: Minimum Number of Moves to Make Palindrome. Difficulty: Hard. Topics: Two Pointers, String, Greedy, Binary Indexed Tree. Asked at Rubrik in the last 6 months.
#276 Paint Fence
LeetCode #276: Paint Fence. Difficulty: Medium. Topics: Dynamic Programming. Asked at Rubrik in the last 6 months.
## Problem Given app dependencies, determine a valid installation order or detect circular dependencies. ## Likely LeetCode equivalent Similar to LC 207 Course Schedule. ## Tags graph, topological_sort, rubrik
## Problem Implement a fixed-capacity circular buffer queue that supports `enqueue`, `dequeue`, `peek`, and `is_full`/`is_empty` operations. When full, the enqueue operation should overwrite the oldest element (ring buffer semantics). The buffer should be O(1) for all operations. ```python class BufferQueue: def __init__(self, capacity: int): ... def enqueue(self, item) -> None: ... def dequeue(self): # returns item or raises if empty def peek(self): # returns next item without removing def is_full(self) -> bool: ... def is_empty(self) -> bool: ... def __len__(self) -> int: ... ``` **Example:** ``` bq = BufferQueue(3) bq.enqueue(1); bq.enqueue(2); bq.enqueue(3) bq.is_full() -> True bq.enqueue(4) # overwrites oldest: [2,3,4] bq.dequeue() -> 2 bq.dequeue() -> 3 bq.is_empty() -> False ``` ## Follow-ups 1. How do you distinguish a full buffer from an empty buffer when using only head and tail pointers (the classic off-by-one problem)? 2. What is the difference between overwrite-on-full semantics and block-on-full semantics, and where is each used? 3. How would you make this thread-safe for a single producer and single consumer without a mutex (lock-free SPSC queue)? 4. How is this data structure used in audio/video streaming pipelines (e.g., jitter buffers)?
## Problem Compute total file sizes in a directory tree structure, summing sizes recursively through subdirectories. ## Likely LeetCode equivalent No confident LC match. ## Tags binary_tree, recursion, rubrik, filesystem
## Problem Design a leaderboard system for a multiplayer game that supports: updating a player's score, querying a player's current rank (1-indexed, rank 1 = highest score), and fetching the top K players. Scores can only increase. ```python class GameRanking: def __init__(self): ... def add_score(self, player_id: str, score: int) -> None: ... def get_rank(self, player_id: str) -> int: ... def top_k(self, k: int) -> list[tuple[str, int]]: ... # top_k returns [(player_id, score)] sorted by score desc ``` **Example:** ``` gr = GameRanking() gr.add_score("Alice", 500) gr.add_score("Bob", 800) gr.add_score("Carol", 800) gr.add_score("Alice", 300) # Alice total = 800 (if cumulative) or 500 gr.get_rank("Alice") -> 2 or 3 depending on design gr.top_k(2) -> [("Bob",800),("Carol",800)] or similar ``` **Clarify:** Does `add_score` replace the score or add to it? ## Follow-ups 1. How would you implement `get_rank` efficiently -- what data structure supports O(log n) rank queries (e.g., sorted set, order-statistics tree)? 2. How does Redis's ZSET (sorted set) solve this problem in production, and what are its rank/score query commands? 3. What is the time complexity of your top_k query, and how does it change if scores are bounded integers (e.g., 0-10000)? 4. How would you shard the leaderboard across multiple servers for 100 million players?
Smallest Difference: Find the Pair of Elements from Two Arrays with Minimum Absolute Difference
## Problem Given two integer arrays `A` and `B`, find the pair `(a, b)` where `a` is from `A` and `b` is from `B` such that `|a - b|` is minimized. Return the pair and the difference. ```python def smallest_difference( A: list[int], B: list[int] ) -> tuple[int, int, int]: # (a, b, difference) pass ``` **Example:** ``` A = [1, 3, 15, 11, 2] B = [23, 127, 235, 19, 8] -> (11, 8, 3) # |11-8| = 3, smallest possible A = [-1, 5, 10, 20, 28, 3] B = [26, 134, 135, 15, 17] -> (28, 26, 2) ``` ## Round 1 - Coding Solve with a two-pointer approach after sorting both arrays. ## Follow-ups 1. What is the time complexity of the two-pointer approach vs. brute force? Why does sorting enable the two-pointer technique here? 2. If there are multiple pairs with the same minimum difference, how do you return all of them? 3. How would you solve this if elements are floating-point numbers (with precision concerns)? 4. Extend the problem: find the triplet `(a, b, c)` from three arrays minimizing `|a - b| + |b - c|`.
## Problem Implement a key-value store that supports snapshot and rollback operations. ## Likely LeetCode equivalent Similar to LC 1146 Snapshot Array. ## Tags hash_table, design, rubrik, snapshot
## Problem Implement a function that expands `${VARIABLE}` placeholders in a template string using a provided variable map. Undefined variables should raise an error. Nested expansions (a variable whose value contains another `${...}`) should be recursively resolved up to a max depth. ```python def expand_variables( template: str, variables: dict[str, str], max_depth: int = 10 ) -> str: pass ``` **Example:** ``` variables = {"NAME": "Alice", "GREETING": "Hello, ${NAME}!", "CITY": "NYC"} expand_variables("${GREETING} From ${CITY}.", variables) -> "Hello, Alice! From NYC." expand_variables("Hi ${UNKNOWN}", variables) -> raises KeyError or UndefinedVariableError # Cycle: A -> "${B}", B -> "${A}" -> raises RecursionDepthError after max_depth ``` ## Follow-ups 1. How do you detect cycles in variable expansion, and what error do you raise? 2. How would you support default values with `${VAR:-default}` syntax (similar to bash)? 3. What is the difference between eager expansion (expand everything at parse time) and lazy expansion (expand at use time)? 4. How would you make the function safe against injection attacks if variable values come from untrusted user input?
What Rubrik Looks for in Software Engineer Interviews
Rubrik Software Engineer interviews are calibrated against the level and scope expected of the role. Across 15+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Rubrik Software Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Rubrik's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Rubrik Software Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Rubrik Software Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Rubrik Software Engineer interview retrospectives on LeakCode.
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