Sigmacomputing·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Interviewed for a software engineer role at Sigmacomputing and got a data aggregation problem that seemed straightforward but had a decent follow-up layer to it. The pivot table framing was a bit unusual compared to typical coding rounds.

Questions Asked (1)

Implement a function that builds a pivot table from a list of records, where each record has a row key, a column key, and a numeric value. Aggregate by the row/column pair and return a structure that supports efficient lookup. Then explain how you'd extend it to support average and minimum in addition to sum.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

Started with a nested dict keyed by row then column, which felt right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a nested dictionary (or hash map of hash maps) for O(1) average lookup, aggregating values as you iterate through records. For extension to average and minimum, discuss maintaining additional state (count, sum, min) per cell and updating them incrementally, or using a strategy pattern for different aggregations.

Pro tip: Mention that for average you need to track count and sum separately, and for minimum you need to initialize with infinity or the first value; also consider whether you need to support dynamic aggregation types or if they are fixed at build time.

1. Clarify requirements and edge cases

Ask about input size, expected operations (lookup frequency, updates), and whether row/column keys are strings or other types. Consider empty records, duplicate keys, and non-numeric values.

2. Design the data structure

Propose a nested dictionary: outer key = row key, inner key = column key, value = aggregated result. This gives O(1) average lookup. Alternatively, use a single dictionary with tuple keys.

3. Implement aggregation for sum

Iterate through records, and for each, update the nested dictionary by adding the value to the existing sum (or initializing if absent).

4. Extend to average and minimum

For average, store both sum and count per cell, then compute average on lookup or maintain running average. For minimum, store the current min and update it when a smaller value is encountered.

5. Discuss trade-offs and optimizations

Compare nested dict vs. tuple-key dict vs. custom class. Discuss memory vs. speed, and whether to precompute aggregates or compute on demand. Mention handling of missing cells.

Key Points to Mention

Time complexity: O(n) to build, O(1) average lookup.
Space complexity: O(r*c) where r and c are distinct row and column keys.
Use of nested dictionaries or a single dictionary with tuple keys.
For average: maintain sum and count, compute average on demand or incrementally.
For minimum: initialize with infinity or first value, update on smaller values.
Consider using a strategy pattern or function pointers to support multiple aggregations cleanly.

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