← Instacart Interview Insights
I picked something around order frequency per customer and talked through retention logic, but I fumbled the second half about where the data actually lives.
Pick a metric that directly ties to Instacart's core business (e.g., order frequency or basket size), then walk through a structured plan for defining, instrumenting, and analyzing it. Show you can bridge product thinking and engineering execution by describing the data pipeline and validation steps.
Pro tip: Anchor your metric to a specific user behavior or business goal, and mention how you'd validate the data (e.g., A/B test or cohort analysis) to show you think beyond just logging events.
Select a metric that aligns with Instacart's key objectives, such as customer retention, order frequency, or average basket size. Briefly justify why it matters.
Specify the exact calculation, time window, and segmentation (e.g., weekly active users who place at least one order). Clarify any edge cases.
Map out where the data comes from: client events, server logs, database tables, or third-party tools. Describe what events or fields need to be logged.
Explain how data flows from collection to analysis: ETL/ELT processes, data warehouse, and any transformations needed. Mention tools like Kafka, Airflow, or Snowflake.
Describe how you'd query and visualize the metric, validate data quality, and use it to inform decisions. Include A/B testing or cohort analysis if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.