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Shopify·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Shopify data scientist interview with a SQL-heavy analytical round. Two questions, both centered on a mobile theme platform scenario. Nothing too wild but you really need to think about what 'growth' actually means before writing a single line of code.

Questions Asked (2)

Q1

Define a metric to measure usage growth for a specific theme, then write SQL that produces that metric broken out by calendar month.

Product Analytics & MetricsData Modeling
Author's notes

The part that tripped me up was 'define a metric' before writing any SQL.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business context and defining 'usage growth' for the theme—e.g., month-over-month growth in active users or sessions. Then outline the SQL logic: aggregate monthly usage, compute growth rates using window functions, and present the metric broken out by calendar month. Emphasize that the metric should be actionable and aligned with Shopify's goals.

Pro tip: Mention that you would validate the metric with stakeholders and consider seasonality (e.g., holiday shopping) when interpreting growth, showing business acumen beyond technical SQL.

1. Clarify the metric

Define what 'usage' means for the theme (e.g., active users, sessions, actions) and what 'growth' means (e.g., month-over-month percentage change). Confirm with the interviewer if needed.

2. Identify data sources

Determine the relevant tables (e.g., events, sessions, users) and how to filter for the specific theme (e.g., theme_id or theme_name).

3. Aggregate monthly usage

Write a subquery or CTE to count the chosen usage metric per calendar month, ensuring proper date truncation and filtering.

4. Compute growth rate

Use window functions (e.g., LAG) to calculate the month-over-month growth percentage, handling nulls for the first month.

5. Present results

Select the month, usage count, and growth rate, ordered chronologically, and explain how to interpret the output.

Key Points to Mention

  • Definition of active usage (e.g., distinct users, sessions, or events) and why it matters for the theme.
  • Month-over-month growth calculation using SQL window functions like LAG.
  • Handling of edge cases: first month (no previous month), missing months, and seasonality.
  • Importance of filtering by theme and date range to avoid noise.
  • Potential need for normalization (e.g., growth relative to total platform usage) to isolate theme performance.
  • Alignment with business goals: how this metric informs product decisions at Shopify.

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

Q2

Write SQL to show non-cumulative monthly revenue for the pirate theme.

Product Analytics & Metrics
Author's notes

Easier of the two.

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AI HintsAI Generated

Suggested Approach

First clarify the schema and definitions: identify the revenue table, the date column, and how to filter for the pirate theme (e.g., a theme column or product category). Then write a SQL query that groups revenue by month and sums it, ensuring it's non-cumulative (i.e., monthly totals, not running totals).

Pro tip: Always confirm whether 'revenue' means gross or net, and whether you need to handle partial months or time zones. Also, consider if the theme is identified via a product attribute or a separate mapping table.

1. Clarify requirements and schema

Ask about the table structure, revenue definition, and how the pirate theme is identified. Confirm the desired time granularity (monthly) and that non-cumulative means each month's total revenue independently.

2. Identify relevant tables and joins

Determine which tables contain revenue data and theme information. If theme is in a separate table, plan the necessary join (e.g., on product_id).

3. Filter for pirate theme

Apply a WHERE clause to include only rows where the theme is 'pirate' (or equivalent). Be mindful of case sensitivity and potential variations in theme naming.

4. Aggregate revenue by month

Use DATE_TRUNC or equivalent to group by month, and SUM the revenue. Ensure the grouping is by month and year to avoid mixing years.

5. Format and present results

Order by month chronologically and alias columns clearly. Optionally, round revenue to two decimal places for readability.

Key Points to Mention

  • Use DATE_TRUNC('month', order_date) to group by month.
  • Filter for pirate theme using a WHERE clause on the appropriate column (e.g., theme = 'pirate').
  • Sum revenue with SUM(revenue) and group by the truncated date.
  • Ensure non-cumulative by not using window functions like SUM() OVER (ORDER BY month).
  • Consider joining tables if theme information is in a separate table.
  • Order results by month to show trend over time.

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