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Instacart·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

SQL-heavy technical screen for a data scientist role at Instacart. One question, focused on time-series aggregation over order data. Nothing too wild but you need to be comfortable with date functions or you'll fumble it.

Questions Asked (1)

Q1

Write a SQL query that returns total revenue and order count for the last 8 full calendar weeks, filtering to standard delivery orders only.

Product Analytics & MetricsData Modeling
Author's notes

The filter part is easy, the tricky bit is getting 'last 8 full calendar weeks' right.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the definition of 'last 8 full calendar weeks' (e.g., weeks starting Monday or Sunday, excluding the current incomplete week) and 'standard delivery orders' (e.g., delivery_type = 'standard'). Then write a query that filters orders to the date range, groups by week, and aggregates revenue and order count. Use a CTE or subquery to handle date boundaries cleanly.

Pro tip: Always confirm the week-start convention and time zone with the interviewer; a one-day offset can change results. Also, consider whether to include weeks with zero orders—if so, use a calendar table or generate_series to left join and fill gaps.

1. Clarify requirements and assumptions

Ask about the definition of 'full calendar weeks' (start day, time zone) and 'standard delivery orders' (specific status or type). Confirm whether to include weeks with no orders and the expected output format (e.g., one row per week or overall totals).

2. Determine date boundaries

Calculate the start and end dates for the last 8 full weeks. For example, if weeks start on Monday, find the most recent Monday that begins a full week (i.e., the Monday of the previous week) and go back 7 weeks to get the start date.

3. Filter and aggregate data

Write the main query to filter orders to the date range and standard delivery, then group by week (using DATE_TRUNC or equivalent) and compute SUM(revenue) and COUNT(order_id).

4. Handle missing weeks (if needed)

If the interviewer wants all 8 weeks even with zero orders, use a calendar table or generate_series to create a complete list of weeks and LEFT JOIN the aggregated results.

5. Review and optimize

Check for edge cases (e.g., time zone conversions, null values) and consider indexing on date and delivery_type for performance. Present the final query clearly.

Key Points to Mention

  • Definition of 'full calendar weeks': typically weeks that have ended, excluding the current partial week, with a specified start day (e.g., Monday).
  • Time zone considerations: ensure dates are in the correct time zone (e.g., UTC or local) to avoid off-by-one errors.
  • Filtering for standard delivery: use the appropriate column (e.g., delivery_type = 'standard') and confirm if there are other conditions like order status = 'completed'.
  • Aggregation functions: SUM for revenue and COUNT for order count, with proper handling of NULLs (e.g., COALESCE for revenue).
  • Grouping by week: use DATE_TRUNC('week', order_date) or equivalent to group orders into weeks.
  • Handling weeks with no orders: use a calendar table or generate_series to ensure all 8 weeks are represented, if required.

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