A reported Waymo Data Analyst recruiter screen used a vehicle dataset and medium-to-hard SQL questions, including an efficiency-ranking query based on miles driven per kWh consumed.
$111K
Avg. Base Comp
$152K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
The reported Waymo Data Analyst interview began with a recruiter screening call, but it was not limited to background discussion. The interviewer shared a vehicle database with three tables and asked two or three SQL questions described as medium to hard. Prepare to write SQL from a supplied schema, not just talk through an approach.
One reported prompt defined vehicle efficiency as total miles driven divided by total kWh consumed and asked for the five most efficient vehicle models. Practice turning that definition into a query: identify the joins and aggregation needed by the provided tables, calculate the ratio at the correct model level, rank the results, and return the top five. Be ready to explain how your query handles the relationship between miles and energy use rather than assuming a precomputed efficiency column.
The available account covers one screening call only, so later stages and overall timing are not reported.
Synthesized from 1 candidate report by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Waymo process.
It was a recruiter screening call which included production level sql questions. There was no guidance on what to except from recruiter screening. The interviewer shared a sample dataset and asked 2, 3 SQL questions that were medium to hard.
Questions asked: Interviewer shared a vehicle database consisting of 3 tables, asked simple to complex sql based on the dataset.
Write a SQL query to return the top 5 most efficient vehicle models, where efficiency is defined as total miles driven / total kWh consumed (miles per kWh).
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Waymo
Design an end-to-end ML system for personalized car recommendations at Cars.com, supporting real-time and batch inference, data distribution shift detection, and CI/CD for frequent model updates.
| Question | |
|---|---|
| Location Frequency | |
| Shortest Path Algorithms | |
| Statistically Significant Test | |
| Empty Neighborhoods | |
| 2nd Highest Salary | |
| Employee Salaries | |
| Top Three Salaries | |
| Last Transaction | |
| First to Six | |
| Top 3 Users | |
| Experiment Validity | |
| User Experience Percentage | |
| First Touch Attribution | |
| Fair Coin | |
| Third Purchase | |
| Button AB Test | |
| Download Facts | |
| Get Top N Frequent Words | |
| Normal Distribution Sample | |
| Size of Joins | |
| 500 Cards | |
| Weighted Keys | |
| Distance Traveled | |
| Network Experiment Design | |
| Raining in Seattle | |
| Assumptions of Linear Regression | |
| Reducing Error Margin | |
| Google Maps Improvement | |
| Type-ahead Search |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports a recruiter screening call that included production-level SQL questions. The account does not describe a separate introductory or behavioral segment, so prepare for technical work during the screening itself.
The interviewer reportedly shared a vehicle database consisting of three tables and used it for a progression from simpler to more complex SQL. Candidates should expect to inspect the supplied schema before choosing joins and aggregations.
A reported question asked for the five most efficient vehicle models, defining efficiency as total miles driven divided by total kWh consumed. Practice grouping at the model level, computing the ratio, ordering it correctly, and limiting the result.
