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Bytedance·Data Analyst·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Bytedance Data Analyst technical screen, two SQL problems back to back. Nothing behavioral, just pure query writing under a bit of time pressure. The problems weren't impossible but the details tripped me up more than I expected.

Questions Asked (2)

Q1

Given a users table and a transactions table, identify users whose total completed crypto transaction amount exceeded £100 within the 7 days following their account creation. Return the user ID and that summed amount.

Product Analytics & MetricsData Modeling
Author's notes

The window join is where I fumbled a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and definitions (e.g., what counts as a 'completed' transaction, timezone handling). Then write a SQL query that joins users to transactions on user_id, filters transactions to those within 7 days of account creation and with status 'completed', groups by user, sums the amount, and filters for sums > £100. Finally, validate edge cases like multiple transactions, currency conversion, and time boundaries.

Pro tip: Explicitly state your assumptions about the data (e.g., transaction timestamps are in UTC, amounts are in GBP) and mention that you'd verify them with the data dictionary or stakeholders before finalizing the query.

1. Clarify requirements and schema

Ask about table structures, definitions of 'completed' and 'crypto transaction amount', timezone, and currency. Confirm the 7-day window is inclusive of the creation date.

2. Filter and join data

Join users and transactions on user_id, and filter transactions to those with status 'completed' and transaction_date between account_creation_date and account_creation_date + 7 days.

3. Aggregate and filter

Group by user_id and sum the transaction amounts, then filter with HAVING sum > 100. Select user_id and the summed amount.

4. Validate and handle edge cases

Check for users with no transactions, multiple transactions, boundary cases (exactly 7 days), and ensure currency consistency. Consider using a subquery or CTE for readability.

Key Points to Mention

  • Definition of 'completed' transaction status and how to filter for it.
  • Time window logic: transaction_date >= account_creation_date AND transaction_date < account_creation_date + INTERVAL '7 days' (or <= depending on inclusivity).
  • Aggregation with SUM and filtering with HAVING clause.
  • Handling of currency: assume all amounts are in GBP or need conversion.
  • Potential need to deduplicate users or transactions if there are duplicates.
  • Performance considerations: indexing on user_id and transaction_date.

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

Q2

Using an activity table that logs views and clicks per product, compute the click-through rate for each product and return whichever product (or products) has the highest CTR. Exclude any product that has zero views.

Product Analytics & MetricsData Modeling
Author's notes

Straightforward conditional aggregation but I second-guessed myself on how to handle ties.

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AI HintsAI Generated

Suggested Approach

First, clarify the table schema and definitions (e.g., what counts as a view vs. click, and whether each row is an event or aggregated). Then, write a SQL query that aggregates views and clicks per product, computes CTR as clicks/views, filters out products with zero views, and finally selects the product(s) with the maximum CTR using a window function or subquery.

Pro tip: Mention that you would handle ties by returning all products with the highest CTR, and discuss how to treat edge cases like products with clicks but zero views (which should be excluded).

1. Clarify requirements and schema

Ask about the table structure, event types, and whether CTR is defined as clicks divided by views. Confirm that products with zero views should be excluded.

2. Aggregate views and clicks per product

Use conditional aggregation (e.g., SUM(CASE WHEN event_type = 'view' THEN 1 ELSE 0 END)) to count views and clicks for each product.

3. Compute CTR and filter zero views

Calculate CTR as clicks/views, ensuring to filter out products where views = 0 to avoid division by zero.

4. Identify product(s) with highest CTR

Use a window function like RANK() or DENSE_RANK() over the CTR in descending order, or a subquery with MAX(CTR), to select the top product(s).

5. Handle ties and present results

If multiple products share the highest CTR, return all of them. Explain how you would format the output (e.g., product ID and CTR).

Key Points to Mention

  • Conditional aggregation to count views and clicks from event logs
  • CTR calculation: clicks / views, with proper handling of division by zero
  • Filtering out products with zero views using HAVING or WHERE clause
  • Using window functions (RANK, DENSE_RANK) or subqueries to find the maximum CTR
  • Handling ties by returning all top products
  • Considering performance implications and indexing on product_id and event_type

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