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CloudTrucks·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026Remote

Summary

SQL screen for a Data Scientist role at CloudTrucks, working with logistics tables for a trucking marketplace. The question was meaty enough that I had to think carefully about window functions and a few edge cases I almost missed.

Questions Asked (1)

Q1

Given tables for jobs, loads, and drivers in a trucking marketplace, write a SQL query that: (1) finds the top 5 states by frequency in the loads table, then within those states identifies which has the highest count of distinct completed jobs; (2) calculates the average rate_per_mile per delivered_date for completed jobs, excluding rows where miles is zero; and (3) produces a running cumulative count of completed jobs by delivered_date using a window function.

Data ModelingProduct Analytics & MetricsAlgorithms & Data Structures
Author's notes

Three parts in one question, which I did not love.

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AI HintsAI Generated

Suggested Approach

Break the problem into three independent parts: first, use a subquery to find the top 5 states by load frequency, then join with completed jobs to find the state with the highest distinct job count; second, filter completed jobs with non-zero miles and compute average rate_per_mile per delivered_date; third, use a window function to compute a running cumulative count of completed jobs ordered by delivered_date. Write each part as a separate query or CTE, and clearly explain your assumptions about table schemas and join keys.

Pro tip: Always clarify the schema and definitions (e.g., what 'completed' means, how rate_per_mile is calculated) before writing SQL; interviewers value candidates who ask smart questions and state assumptions rather than jumping to code.

1. Clarify schema and definitions

Ask about table structures, join keys, and what 'completed' means (e.g., status column). Confirm whether rate_per_mile is a column or needs calculation from rate and miles.

2. Part 1: Top states and highest distinct completed jobs

Use a subquery to get the top 5 states by load count. Then join loads with jobs (filtered to completed) and count distinct job IDs per state, selecting the state with the maximum count.

3. Part 2: Average rate_per_mile per delivered_date

Filter completed jobs with miles > 0, then group by delivered_date and compute AVG(rate_per_mile). Ensure rate_per_mile is correctly derived if not a direct column.

4. Part 3: Running cumulative count of completed jobs

Use a window function like SUM(COUNT(*)) OVER (ORDER BY delivered_date) or COUNT(*) OVER (ORDER BY delivered_date) to get a running total of completed jobs per delivered_date.

5. Combine and validate

Present the three queries separately or as CTEs, and discuss how you would validate results (e.g., check for nulls, ensure date ranges make sense).

Key Points to Mention

  • Use of subqueries or CTEs to modularize the query and improve readability.
  • Handling of distinct counts: COUNT(DISTINCT job_id) to avoid duplicates.
  • Filtering conditions: status = 'completed' and miles > 0.
  • Window functions: OVER (ORDER BY delivered_date) for running totals.
  • Assumptions about table schemas and join keys (e.g., loads.driver_id = drivers.driver_id, jobs.load_id = loads.load_id).
  • Performance considerations: indexing on state, delivered_date, and status.

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