Canva Data Scientist candidates report a recruiter screen, a SQL-heavy technical round, and a longer final block covering Python, stats, product metrics, and ML or system design. Here is what to prepare.
$140K
Avg. Base Comp
$190K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describing Canva's Data Scientist process most often point to SQL as the core filter. One candidate reported a one-hour advanced SQL round covering window functions and the complexity of joins. Another described a practical SQL interview with joins, aggregations, HAVING, NULL handling, CTEs, and a prompt to return every user with their number of created designs, including users with none. A third said the SQL test was done on a notepad rather than in an IDE, with window functions and date reshaping. Practice writing queries without autocomplete and explain your edge cases out loud.
Product thinking also comes up. One candidate said final-round questions tied to Canva's product and user data, such as measuring feature adoption or evaluating a change to search. Another reported A/B experiments, event analytics, and metric definitions. Know how Canva is used well enough to propose sensible metrics.
Beyond SQL, reports vary. One candidate described a 3.5 to 4 hour technical block covering Python, modeling, system design, and behavioral questions, plus implementing clustering from scratch and an ML system design for a classification problem with human review. Another reported a roughly 4-hour final block covering SQL, Python, statistics, and behavioral questions. The reports we have all ended without an offer or with a pending result, so use them as preparation material rather than a prediction of difficulty. Candidates also describe interviewers as patient and willing to answer clarifying questions.
Synthesized from 6 candidate reports by our editorial team.
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Real interview reports from people who went through the Canva process.
I had a first-round interview with HR, where we discussed my resume, the role, and how my experience aligned with the position. The HR representative also explained the Data Science team structure and the types of projects they work on.
The next stage was a SQL-focused technical interview. I was given practical SQL questions involving joins, aggregations, GROUP BY, HAVING, handling NULL values, and calculating metrics from multiple tables. I had to explain my approach and write queries to solve the problems rather than simply describe SQL concepts. The interviewer also asked follow-up questions to test my understanding of why I chose a particular approach and how I would handle different edge cases.
Questions included SQL, CTEs, window functions, joins, A/B experiments, event analytics, and metric definition. One prompt asked me to write a SQL query returning every user with the number of designs they created, while identifying users who had never created a design.
Prep tip from this candidate
Practice writing SQL that preserves users with no matching records, then explain your join choice, edge cases, and metric logic. Review A/B experiment basics, event analytics, and metric definitions.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Canva
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| Search Ranking | |
| Generate Shopping List from Recipes | |
| Hurdles In Data Projects | |
| Duplicate Rows | |
| Digital Marketing Metrics | |
| International e-Commerce Warehouse | |
| Electricity Supply | |
| k-Means from Scratch | |
| Statistically Significant Test | |
| 2nd Highest Salary | |
| Merge Sorted Lists | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| First to Six | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Experiment Validity | |
| Download Facts | |
| Monthly Customer Report | |
| Upsell Transactions | |
| First Touch Attribution | |
| Random SQL Sample | |
| Button AB Test | |
| Compute Deviation | |
| Top 3 Users | |
| Find the First Non-Repeating Character in a String | |
| String Shift |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening call with a recruiter or HR contact that focuses on your background, why you want Canva, and how the role fits. One candidate also said the team structure and typical projects were explained. Expect to be asked about Canva's product and be ready with a specific answer.
Candidates describe a SQL-focused interview involving window functions, joins, aggregations, HAVING, NULL handling, and CTEs. One candidate was asked to list every user with their design count, including users with none. Others reported writing queries without an IDE and explaining join complexity, so practice reasoning aloud.
Some candidates mention a timed online assessment. One described a HackerRank stage with easy-to-medium coding and multiple-choice stats and data science questions. A graduate-level candidate reported an Alooba assessment with multiple-choice, data analysis, and SQL sections. This stage may vary by role level, so confirm the format with your recruiter.
Candidates report a long final session of roughly 3.5 to 4 hours, in one case possibly split across two days. Reported content includes SQL, Python, statistics, modeling, behavioral questions, and product-metric cases such as measuring feature adoption. One candidate was asked to implement clustering from scratch.
Candidates describe a design discussion in or near the final stage. One was given an ML system design for a classification problem with human intervention. Another, at a lead level, was asked to design a data pipeline and justify trade-offs. Prepare to defend your choices under follow-up questions, not only name tools.
