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Sigmacomputing·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

Coding round at Sigmacomputing for a software engineer role. The main problem was building a pivot table from scratch, which sounds straightforward but has a lot of moving parts once they start asking follow-ups about aggregation, missing cells, and complexity.

Questions Asked (1)

Q1

Implement a basic pivot table: given a list of records with multiple fields, and a pivot specification defining row fields, column fields, and value fields with aggregation functions (sum, count, average, min, max), produce a 2D pivot table with aggregated values per (row-key, column-key) cell. Discuss data structures, missing cell handling, header sorting, multiple aggregations, grand totals, and time/space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

This is the kind of problem that feels manageable until you start coding and realize how many edge cases you glossed over.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the pivot specification and data characteristics, then outline a hash-based aggregation approach using composite keys for (row, column) pairs. Discuss how to handle missing cells, sorting headers, multiple aggregations, and grand totals, and analyze time/space complexity.

Pro tip: Mention that you would use a dictionary with tuple keys for O(1) aggregation, and pre-sort headers to avoid repeated sorting. Also, discuss how to handle missing cells by either omitting them or filling with a default value based on the aggregation function.

1. Clarify Requirements and Assumptions

Ask about the expected size of data, whether multiple aggregations per value field are needed, and how to handle missing cells (e.g., null vs. zero). Confirm if grand totals are required and if header sorting is necessary.

2. Design Data Structures

Use a hash map (dictionary) with composite keys (row_key, column_key) to store aggregated values. For multiple aggregations, store a list or struct per key. Maintain separate sets for row and column headers to sort later.

3. Aggregate Data

Iterate through records, extract row and column keys, and update the aggregation for each value field. For sum, count, average, min, max, maintain appropriate accumulators (e.g., sum and count for average).

4. Build Pivot Table and Handle Missing Cells

Sort row and column headers. Create a 2D array or nested dictionary. For each (row, column) pair, retrieve the aggregated value; if missing, decide on representation (e.g., None, 0, or empty string) based on aggregation type.

5. Compute Grand Totals and Analyze Complexity

Compute row totals, column totals, and overall total by aggregating across the pivot table or during the initial pass. Discuss time complexity O(N) for aggregation plus O(R*C) for table construction, and space O(R*C) for the output.

Key Points to Mention

  • Use of hash map with composite keys for O(1) aggregation.
  • Handling missing cells: omit, fill with zero, or null based on aggregation semantics.
  • Sorting row and column headers for consistent output.
  • Support for multiple aggregations per value field (e.g., sum and average).
  • Grand totals computation: row totals, column totals, and overall total.
  • Time complexity: O(N) for processing records, O(R*C) for building table; space complexity: O(R*C) for output.

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