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Shopify·Data Scientist·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Shopify data scientist interview with a SQL-heavy analytical question built around a made-up mobile app schema. The problem was open-ended in a way that felt a bit disorienting at first since you had to define your own metric before you could even start writing code.

Questions Asked (1)

Q1

Given tables for themes, user activity, revenue, users, and a calendar, write a SQL query that tracks whether Pirate-themed content is growing over time. You need to define your own growth metric, return a month-by-month time series suitable for charting, and explain why your chosen metric captures growth.

Product Analytics & MetricsData ModelingPricing & Monetization
Author's notes

The open-ended metric definition part tripped me up more than the SQL itself.

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AI HintsAI Generated

Suggested Approach

Start by defining a growth metric that normalizes Pirate-themed activity against overall platform activity, such as the share of monthly active users engaging with Pirate themes or the Pirate theme's share of total revenue. Then write a SQL query that aggregates this metric by month using the calendar table, and explain why this normalized metric isolates true growth from overall platform growth.

Pro tip: Mention that you would validate the metric by checking for seasonality or one-time events (e.g., a movie release) and consider using a rolling average or year-over-year comparison to smooth noise.

1. Define the growth metric

Choose a metric that captures Pirate-themed engagement relative to the overall platform, such as the percentage of monthly active users who interact with Pirate content or the Pirate theme's share of total revenue.

2. Identify relevant tables and joins

Determine which tables contain theme information, user activity, revenue, and dates, and plan the necessary joins to link Pirate-themed content to user actions and revenue.

3. Write the SQL query

Construct a query that aggregates the chosen metric by month, using the calendar table to ensure a complete time series, and filters for Pirate-themed content.

4. Explain why the metric captures growth

Articulate how the metric reflects changes in Pirate-themed engagement over time, independent of overall platform growth, and why it is suitable for tracking growth.

5. Consider additional analyses

Optionally, discuss how you would validate the metric, handle seasonality, or compare with other themes to provide context.

Key Points to Mention

  • Normalization: using a ratio (e.g., Pirate users / total users) to isolate theme-specific growth from platform-wide trends.
  • Time series completeness: using the calendar table to generate a continuous month-by-month series, even for months with no Pirate activity.
  • Metric definition: clearly stating what constitutes 'Pirate-themed content' and 'engagement' (e.g., views, purchases, active users).
  • SQL techniques: using LEFT JOINs, GROUP BY month, and window functions if needed for rolling averages.
  • Business relevance: explaining how the metric informs decisions about content investment or marketing.
  • Validation: mentioning checks for data quality, seasonality, or external events that could skew the metric.

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