← Instacart Interview Insights

Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Instacart software engineer round that was basically one big coding and design question wrapped together. The problem sounded like a data wrangling exercise but they kept pushing on edge cases and scalability until it felt like a system design conversation.

Questions Asked (2)

Q1

Given a list of records each containing a date string, store ID, a metric name, and a value, write code to pivot the data so each (store, date) pair becomes one row with columns for sales, refunds, and visits. Missing metrics should default to 0, rows should be sorted by date ascending per store, and you cannot use any date or time libraries.

Algorithms & Data StructuresData Modeling
Author's notes

The no-date-library constraint is where I almost tripped myself up.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, clarify the input format and constraints, especially the date string format and the no-date-library restriction. Then, outline a two-pass approach: group records by (store, date) while aggregating metrics, and then sort each store's groups by date using string comparison. Finally, write clean code with helper functions and test edge cases.

Pro tip: Mention that you can sort dates as strings if they are in ISO format (YYYY-MM-DD), which is lexicographically sortable. This shows you understand the constraint and can leverage it without date libraries.

1. Clarify requirements and constraints

Ask about the date string format, whether multiple records for the same (store, date, metric) exist, and how to handle missing metrics. Confirm that no date libraries means you cannot parse dates into date objects.

2. Design data structures

Choose a dictionary keyed by (store, date) to accumulate metric values, and a set of all metric names to ensure all columns are present. Consider using a defaultdict for simplicity.

3. Aggregate records

Iterate through the input list, and for each record, add its value to the corresponding (store, date) entry under the metric name. Initialize missing metrics to 0 when creating a new entry.

4. Sort and format output

For each store, sort its dates in ascending order using string comparison (assuming ISO format). Then, for each date, output a row with the store, date, and the values for sales, refunds, and visits (defaulting to 0 if absent).

5. Test and handle edge cases

Test with empty input, missing metrics, multiple records for the same metric, and stores with no records. Ensure the output is sorted correctly and all required columns are present.

Key Points to Mention

  • Use a dictionary keyed by (store, date) to group records and accumulate metric values.
  • Leverage lexicographic sorting of ISO date strings (YYYY-MM-DD) to sort dates without date libraries.
  • Initialize missing metrics to 0 when creating a new (store, date) entry.
  • Handle multiple records for the same (store, date, metric) by summing values.
  • Ensure output rows are sorted by date ascending per store.
  • Write clean, modular code with helper functions for readability and testability.

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

Q2

How would you handle malformed date strings, duplicate entries for the same store, date, and metric combination, and inputs too large to fit in memory?

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

Three sub-questions stapled together, which I did not love.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the data pipeline context and requirements, then systematically address each issue: malformed dates, duplicates, and memory constraints. Propose concrete strategies for each, emphasizing trade-offs and validation, and tie them together into a robust ingestion process.

Pro tip: Demonstrate awareness of data quality metrics and monitoring; suggest logging malformed records and duplicate counts to track pipeline health over time.

1. Clarify Requirements and Context

Ask about the data source, expected volume, acceptable error rates, and downstream usage to tailor your approach.

2. Handle Malformed Dates

Propose validation with fallback parsing (e.g., multiple date formats), and decide whether to reject, correct, or quarantine invalid records.

3. Deduplicate Entries

Define a unique key (store, date, metric) and choose a deduplication strategy: keep first/last, aggregate, or flag conflicts, using a hash set or database constraint.

4. Process Large Inputs

Use streaming or chunked processing to handle data that doesn't fit in memory, leveraging external sorting, disk-based hash maps, or distributed frameworks.

5. Integrate and Validate

Combine the strategies into a pipeline with monitoring, logging, and idempotency to ensure reliability and observability.

Key Points to Mention

  • Date parsing libraries and handling multiple formats (e.g., ISO 8601, epoch timestamps)
  • Deduplication techniques: hash set, Bloom filter, or SQL UPSERT with unique constraints
  • Streaming algorithms and external sorting for memory efficiency
  • Trade-offs between strict validation and data loss, and between in-memory vs. disk-based processing
  • Idempotency and exactly-once processing semantics
  • Monitoring and alerting for data quality issues

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