Bloomberg Coding Interview Questions
32 Bloomberg coding interview problems with full optimal solutions — 16 easy, 13 medium, 3 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Bloomberg interviewer values, and a FAQ section.
Showing 13 problems of 32
- #146mediumfoundational
146. LRU Cache
Bloomberg Terminal caches thousands of live ticker snapshots in memory — this problem tests whether you can build the eviction policy that keeps the most-recently-accessed instruments hot while dropping stale ones in O(1) per operation.
- #153mediumfoundational
153. Find Minimum in Rotated Sorted Array
Bloomberg's market-data systems store price histories as circular buffers that reset at midnight — finding the rotation pivot is the same binary search insight Bloomberg engineers apply when locating the session-open price in a ring buffer.
- #239mediumfoundational
239. Sliding Window Maximum
Bloomberg's risk engine computes rolling maximum drawdown across a moving time window on live price feeds — this problem tests whether you can deliver those rolling stats in O(n) with a monotonic deque instead of rescanning the window each tick.
- #347mediumfoundational
347. Top K Frequent Elements
Bloomberg's market-intelligence dashboards surface the top-K most actively traded tickers across millions of events per session — this problem tests whether you can identify those leaders in O(n log k) using a min-heap rather than sorting the full frequency table.
- #560mediumfoundational
560. Subarray Sum Equals K
Bloomberg's compliance team counts how many consecutive trading windows sum to a target P&L threshold — this is exactly the subarray-sum problem, and the prefix-sum hash-map trick cuts the search from O(n^2) to a single pass every real-time analyst depends on.
- #621mediumfoundational
621. Task Scheduler
Bloomberg's batch-job infrastructure schedules thousands of daily data-refresh tasks under cooldown constraints between repeated runs — this problem captures that scheduling logic and tests whether you can compute minimum total time using a greedy frequency analysis instead of simulation.
- #973mediumfoundational
973. K Closest Points to Origin
Bloomberg's geo-analytics tools identify the K nearest branch offices or client locations to a reference point — this problem tests whether you apply a max-heap of size K for a clean O(n log K) solution instead of sorting the entire dataset when K is small.
- #3mediumcompany favorite
3. Longest Substring Without Repeating Characters
Given a string, find the length of the longest substring without repeating characters. Bloomberg uses this as their canonical sliding-window question — they want a clean O(n) two-pointer with a hash map storing last-seen indices.
3 free resourcesSolve → - #7mediumfrequently asked
7. Reverse Integer
Reverse the digits of a 32-bit signed integer, returning 0 on overflow. Bloomberg uses this specifically to test whether you handle the 32-bit integer overflow check correctly — finance code at Bloomberg routinely cares about numeric boundaries.
3 free resourcesSolve → - #53mediumcompany favorite
53. Maximum Subarray
Find the contiguous subarray with the largest sum. Bloomberg uses Kadane's algorithm as the gateway to dynamic-programming questions — they want to see you derive the recurrence (current_max = max(num, current_max + num)) on the board.
3 free resourcesSolve → - #56mediumcompany favorite
56. Merge Intervals
Given an array of intervals, merge all overlapping intervals. Bloomberg uses this to test the sort-then-sweep pattern that shows up in calendar, market-hours, and time-window problems across their data-infrastructure teams.
3 free resourcesSolve → - #57mediumfrequently asked
57. Insert Interval
Insert a new interval into a sorted list of non-overlapping intervals, merging as needed. Bloomberg uses this as the Merge Intervals follow-up — same shape but O(n) instead of O(n log n) because the input is pre-sorted.
3 free resourcesSolve → - #287mediumfrequently asked
287. Find the Duplicate Number
An array of n+1 integers in [1, n] contains exactly one duplicate. Find it without modifying the array and in O(1) extra space. Bloomberg uses this as a classic 'two constraints, no obvious trick' problem to see if you reach for Floyd's cycle detection.
3 free resourcesSolve →
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