Shopify·Data Scientist·Technical Phone Screen·Senior

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Apr 2026Remote

Summary

Shopify data scientist interview with a heavy SQL focus, specifically around theme piracy analysis. Three connected problems, all in one session, building on the same schema. It felt less like a coding screen and more like a take-home that someone decided to give you live.

Questions Asked (3)

Given a schema tracking theme installations on an e-commerce platform, write SQL to compute a monthly pirated install rate: for each calendar month, find the share of shops that installed at least one pirated theme out of all shops that installed any theme that month.

Product Analytics & MetricsData Modeling

Author's notes

The install vs active distinction tripped me up at first.

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AI HintsAI Generated

Suggested Approach

First, clarify the schema and definitions: what tables exist, how themes are identified as pirated, and what constitutes an installation. Then, write a query that aggregates installations per shop per month, flags shops with at least one pirated installation, and computes the ratio of such shops to all shops with any installation in that month.

Pro tip: Always confirm the grain of the data and whether 'monthly' means calendar month or rolling 30-day period; also check if a shop can have multiple installations in a month and how to handle duplicates.

1. Understand the schema and definitions

Identify the relevant tables (e.g., shops, themes, installations) and columns. Clarify what 'pirated' means (e.g., a flag or a separate table) and what an 'installation' event looks like.

2. Aggregate installations per shop per month

Write a subquery to get distinct shop_id and month combinations where an installation occurred, ensuring you capture all shops that installed any theme.

3. Flag shops with pirated installations

Within the same subquery or a subsequent one, determine for each shop and month whether they installed at least one pirated theme (e.g., using a MAX or EXISTS condition).

4. Compute monthly rates

Group by month and calculate the ratio: count of distinct shops with at least one pirated installation divided by count of distinct shops with any installation.

5. Handle edge cases and validate

Consider months with no installations, shops with multiple installations, and ensure the denominator includes all shops with any theme installation. Validate results with sample data.

Key Points to Mention

Use DISTINCT counts to avoid double-counting shops with multiple installations.
Define 'pirated' clearly—likely a boolean flag or a join to a piracy table.
Ensure the denominator includes all shops that installed any theme, not just those with pirated themes.
Group by calendar month using DATE_TRUNC or equivalent.
Consider using CTEs for readability and to separate aggregation steps.
Check for NULLs or missing data in the piracy flag and handle appropriately.

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

Using the same schema, write SQL to compute a monthly active pirated usage rate: for each month, what fraction of shops with any active theme also had at least one pirated theme active during that month?

Product Analytics & MetricsData Modeling

Author's notes

This is where the interval overlap logic comes in and it's genuinely annoying to get right.

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AI HintsAI Generated

Suggested Approach

Break the problem into two parts: first, identify shops with any active theme per month, and second, among those, identify shops with at least one pirated theme active. Then compute the ratio of the latter to the former for each month. Use a monthly calendar table or generate months from the data to ensure all months are covered.

Pro tip: Clarify the definition of 'active' and 'pirated' upfront—whether a theme is considered active if it was installed and not uninstalled during the month, and how pirated status is determined (e.g., a flag in the theme table). Also, consider edge cases like shops with multiple themes and how to handle partial months.

1. Understand the schema and definitions

Identify the relevant tables (e.g., shops, themes, shop_themes) and columns indicating theme activity and pirated status. Clarify what 'active' means (e.g., installed and not uninstalled) and how pirated themes are flagged.

2. Generate monthly active themes per shop

For each month, determine which themes were active for each shop. This may involve expanding date ranges or using a calendar table to ensure each month is represented.

3. Identify shops with any active theme and with pirated active theme

For each month, flag shops that have at least one active theme (denominator) and shops that have at least one active pirated theme (numerator). Use conditional aggregation or EXISTS clauses.

4. Compute the monthly ratio

For each month, calculate the fraction by dividing the count of shops with pirated active themes by the count of shops with any active theme. Ensure to handle months with zero denominator.

Key Points to Mention

Definition of 'active theme'—likely based on installation date and uninstallation date, or a status flag.
Definition of 'pirated theme'—how it is identified in the schema (e.g., a boolean column or a separate table).
Handling of shops with multiple active themes—use DISTINCT or EXISTS to avoid double-counting.
Time granularity—ensuring each month is covered, possibly using a date dimension table or generating months from min/max dates.
Edge cases: shops with no active themes, months with no active shops, and how to treat them in the ratio.
Performance considerations: using window functions or subqueries to efficiently compute the ratio.

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

Still using the same schema, write SQL to compute monthly and cumulative estimated revenue loss from pirated theme installations, where each active month of a pirated install costs the monthly license fee of the legitimate theme it copies.

Product Analytics & MetricsPricing & MonetizationData Modeling

Author's notes

Favorite part of the whole thing.

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AI HintsAI Generated

Suggested Approach

First, identify the tables and columns representing pirated installations, legitimate themes, and monthly license fees. Then, for each pirated install, join to its copied theme to get the monthly fee, and generate a row for each active month. Finally, aggregate by month to get monthly loss, and use a window function to compute cumulative loss.

Pro tip: Clarify with the interviewer whether 'active month' means any month the install was active for at least one day, or full months only—this assumption significantly affects the calculation and shows attention to detail.

1. Identify relevant tables and columns

Locate the tables for pirated installs (with install date, uninstall date, and copied theme ID) and legitimate themes (with theme ID and monthly license fee).

2. Generate monthly active periods

For each pirated install, create a row for each month it was active, using a date spine or generate_series, and determine the monthly fee from the copied theme.

3. Calculate monthly revenue loss

Sum the monthly fees for all active pirated installs per month to get the total estimated revenue loss for that month.

4. Compute cumulative revenue loss

Use a window function (e.g., SUM() OVER (ORDER BY month)) to calculate the running total of monthly losses across months.

Key Points to Mention

Assumption about what constitutes an 'active month' (e.g., any day active vs. full month).
Handling of installs that span multiple months, including partial months.
Use of a date spine or generate_series to expand installs into monthly rows.
Join between pirated installs and legitimate themes to get the correct monthly license fee.
Aggregation by month to get monthly loss, then window function for cumulative loss.
Potential data quality issues: missing uninstall dates (treat as ongoing), multiple themes per install, etc.

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