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Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Instacart software engineering interview with a coding problem that looked like a simple pivot table exercise but had a sneaky constraint baked in. The no-date-library rule is what made it actually interesting.

Questions Asked (1)

Q1

You're given a stream of transaction records, each with a shopper ID, an ISO date string, and an amount. Produce a pivoted report for a given 7-day window where each shopper gets one row with columns for each day offset (d0 through d6), summing amounts per day and filling missing days with zero. You cannot use any date or datetime libraries. Walk through your data structures, how you handle missing days, and the time and space complexity of your solution.

Algorithms & Data StructuresTechnical Trade-offsData Modeling
Author's notes

The no-library constraint is the whole point of this question and I almost glossed over it.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and window definition, then propose a two-pass solution: first compute the day offset for each record by parsing the ISO date string manually, then aggregate amounts into a hash map keyed by shopper ID and day offset. Finally, pivot the aggregated data into rows with columns d0–d6, filling missing days with zero.

Pro tip: Mention that you would validate the date string format and handle edge cases like invalid dates or out-of-window records early, and discuss how the solution scales if the window size changes from 7 days to N days.

1. Clarify requirements and constraints

Confirm the exact format of the ISO date string, how the 7-day window is defined (e.g., start date provided or derived from data), and whether the output should be sorted or include all shoppers seen.

2. Parse dates without libraries

Manually extract year, month, and day from the ISO string (e.g., 'YYYY-MM-DD') using string slicing and integer conversion, then compute the day offset relative to the window start by converting to a day number (e.g., days since epoch) using arithmetic.

3. Aggregate amounts per shopper per day

Use a hash map (dictionary) where keys are shopper IDs and values are arrays of length 7 (or another map) to accumulate sums for each day offset; skip records outside the window.

4. Pivot and fill missing days

Iterate over the hash map to produce one row per shopper, with columns d0–d6 initialized to zero and populated from the aggregated sums; ensure all shoppers have a row even if they have no transactions in some days.

5. Analyze complexity and trade-offs

State that time complexity is O(n) for n records (assuming constant-time hash operations) and space complexity is O(m * 7) for m shoppers; discuss potential optimizations like using a fixed-size array for days if window size is known.

Key Points to Mention

  • Manual date parsing: extracting year, month, day via string slicing and converting to integers.
  • Day offset calculation: converting dates to a day number (e.g., days since epoch) using arithmetic, then subtracting the window start day number.
  • Hash map aggregation: using a dictionary keyed by shopper ID with an array or inner map for day sums.
  • Handling missing days: initializing all day columns to zero and only updating when transactions exist.
  • Time and space complexity: O(n) time and O(m * 7) space, with n records and m shoppers.
  • Edge cases: invalid date strings, records outside the window, and shoppers with no transactions in the window.

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