Databricks·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026Remote

Summary

Databricks software engineer screen that came down to a single coding problem on run-length encoding. Pretty straightforward as far as these things go, though the follow-up complexity questions tripped me up a bit.

Questions Asked (1)

Implement run-length encoding and decoding for a string of lowercase letters. The encode function should compress consecutive repeated characters into character-count pairs, and decode should reverse that. Also walk through time and space complexity for each, and handle edge cases like empty strings or single-character runs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The encoding part was fine, just iterate and track the current character and a counter.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then implement encode and decode with a single-pass approach using a StringBuilder. After coding, analyze time and space complexity for each function and discuss potential trade-offs.

Pro tip: Mention that run-length encoding is only beneficial for strings with many consecutive repeats; for random strings, it can double the size. This shows you understand practical applications and trade-offs.

1. Clarify requirements and edge cases

Confirm input constraints (lowercase letters only), output format (e.g., 'a3b2'), and how to handle empty strings, single characters, and runs longer than 9. Ask if the encoded string should include counts for single characters (e.g., 'a' vs 'a1').

2. Design encode algorithm

Iterate through the string, counting consecutive identical characters. Append the character and its count to a StringBuilder. Handle the last run after the loop.

3. Design decode algorithm

Iterate through the encoded string, reading a character followed by one or more digits to form the count. Append the character repeated count times to a StringBuilder.

4. Analyze complexity

For encode: O(n) time, O(n) space in worst case (e.g., no repeats). For decode: O(m * k) time where m is number of runs and k is average count, but overall O(n) where n is decoded length; space O(n).

5. Discuss edge cases and trade-offs

Cover empty string, single character, runs >9 (multi-digit counts), and strings with no repeats. Mention that RLE is not always compression; it depends on data.

Key Points to Mention

Time complexity: O(n) for both encode and decode, where n is the length of the input string.
Space complexity: O(n) for output, but encode can be O(1) extra space if modifying in place (not typical).
Edge cases: empty string returns empty string; single character returns 'a1' or 'a' depending on convention.
Handling multi-digit counts: use integer parsing or build count digit by digit.
Trade-off: RLE is effective for data with many repeats, but can increase size for random data.
Use StringBuilder for efficient string concatenation in Java (or equivalent in other languages).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.