Instacart·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Instacart software engineer interview that went deep on data transformation and pandas. The question was meaty enough that I kept second-guessing whether they wanted a code walkthrough or a design discussion, and by the end I think I gave them a weird hybrid of both.

Questions Asked (1)

Given a transactions dataset with user_id, category, subcategory, amount, and an ISO timestamp, implement two pivot table transformations in Python: first, rows as category and columns as YYYY-MM months with summed amounts (zero-filled for missing cells); second, rows as (user_id, category) pairs and columns as subcategory with row counts. Also outline an equivalent SQL approach, discuss null handling, timezone issues, chunking for large data, and a top-K column strategy with an OTHER bucket. State time and space complexity.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

This was a lot to unpack and I kind of froze for a second deciding where to start.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the dataset schema and requirements, then walk through the pandas implementation for both pivots, emphasizing zero-filling and row counts. Follow with the SQL equivalent, discuss edge cases like nulls and timezones, and address scalability with chunking and top-K strategies. Conclude with time/space complexity analysis.

Pro tip: Mention that you would validate the pivot results with a quick sanity check (e.g., total sum before and after) and discuss how you'd handle timezone conversion early to avoid subtle bugs.

1. Clarify requirements and data assumptions

Confirm the dataset structure, expected output format, and any constraints (e.g., timezone, null handling). Ask about data size to tailor the solution.

2. Implement pandas pivot tables

Use groupby and unstack or pivot_table to create the two pivots. For the first, extract YYYY-MM from timestamp, sum amounts, and fill missing with 0. For the second, group by user_id, category, subcategory, count rows, and unstack subcategory.

3. Outline SQL equivalent

Write SQL queries using conditional aggregation (CASE WHEN) or PIVOT (if supported) to achieve the same transformations. Mention that dynamic columns may require dynamic SQL.

4. Discuss edge cases and scalability

Address null handling (e.g., drop or fill), timezone conversion (e.g., to UTC), chunking for large data (e.g., Dask or chunked pandas), and top-K column strategy with an OTHER bucket to limit cardinality.

5. Analyze complexity

State time and space complexity: O(n) time for grouping and pivoting, O(n + k*m) space where k is number of groups and m is number of columns.

Key Points to Mention

Zero-filling missing cells in the first pivot using fillna(0) or reindex.
Using pd.Grouper or dt.to_period('M') for monthly aggregation.
Handling nulls: decide whether to drop, fill, or treat as a separate category.
Timezone: convert timestamps to a consistent timezone (e.g., UTC) before extracting month.
Chunking: use pandas chunksize or Dask for large datasets to avoid memory issues.
Top-K columns: keep top K frequent subcategories and group the rest into 'OTHER' to reduce dimensionality.

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