Datadog·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Datadog SWE interview with a sliding window coding problem. Pretty focused on getting the implementation right under time pressure.

Questions Asked (1)

You have a list of datapoints, each with a set of tags, a timestamp, and a numeric value. Write a function that takes a tag t and a window size k, filters to only the datapoints containing tag t, and returns the sum for every consecutive window of k datapoints from that filtered set.

Algorithms & Data Structures

Author's notes

Took me a minute to realize the windowing happens after filtering, not over the full dataset.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and edge cases first, then propose an efficient solution using a sliding window over the filtered datapoints. Discuss time and space complexity, and consider whether the datapoints are sorted by timestamp or if the window is based on index rather than time.

Pro tip: Mention that if the datapoints are not sorted by timestamp, you might need to sort them first if the window is time-based; but if the window is index-based, sorting is unnecessary. Also, discuss how to handle windows with fewer than k datapoints.

1. Clarify requirements

Ask whether the window is based on consecutive datapoints in the filtered list (index-based) or on a time interval. Confirm the expected output format and how to handle edge cases like fewer than k datapoints.

2. Filter datapoints

Iterate through the list and select only those containing tag t. This can be done in O(n) time.

3. Compute sliding window sums

Use a sliding window approach: initialize the sum of the first k elements, then slide by subtracting the element leaving the window and adding the new element. This yields O(m) time where m is the number of filtered datapoints.

4. Handle edge cases

If the filtered list has fewer than k elements, return an empty list or as specified. Also consider if k is 0 or negative, and whether to return sums as integers or floats.

5. Analyze complexity and optimize

State that the overall time complexity is O(n + m) and space O(m) for the filtered list (or O(1) extra if done in one pass). Discuss potential optimizations if needed.

Key Points to Mention

Time complexity: O(n) for filtering plus O(m) for sliding window, where n is total datapoints and m is filtered count.
Space complexity: O(m) for storing filtered datapoints or O(1) extra if computing on the fly.
Sliding window technique to avoid recomputing sums from scratch for each window.
Edge cases: fewer than k datapoints, k <= 0, empty input, duplicate tags.
Whether the window is index-based or time-based; if time-based, sorting by timestamp may be required.
Potential follow-up: how to handle streaming data or very large datasets that don't fit in memory.

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