Canva Data Engineer Interview Questions + Guide in 2025

Canva is a global design platform that empowers users to create stunning graphics and documents effortlessly through its intuitive interface and extensive template library.

As a Data Engineer at Canva, you will be at the forefront of transforming how data is utilized to enhance user experiences and drive business value. Your primary responsibilities will include designing, developing, and maintaining robust data pipelines that ensure seamless data ingestion and extraction across various platforms. You will collaborate closely with cross-functional teams, including Product Managers and Technical Program Managers, to understand user needs and deliver self-service capabilities for data platform users.

A successful candidate will possess strong experience in data platform engineering, especially in cloud environments like AWS, and have a solid foundation in software engineering practices. Proficiency in SQL and familiarity with data platform tooling such as Snowflake, DBT, and Fivetran will be critical. You should also be comfortable with CI/CD processes and have a proactive approach to optimizing performance and reliability of data solutions.

Understanding Canva's culture of continuous improvement and innovation will set you apart. The role demands a proactive mindset to explore emerging technologies, ensuring that the data solutions you design not only meet current requirements but also anticipate future challenges.

This guide aims to equip you with insights and strategies to excel in your Canva Data Engineer interview by highlighting essential skills and preparing you for the types of questions you may encounter.

The interview process for a Data Engineer role at Canva is structured and involves multiple stages designed to assess both technical skills and cultural fit. Here’s a breakdown of what you can expect:

1. Initial Screening

The process begins with an initial screening interview, typically conducted by a recruiter. This conversation lasts about 30-60 minutes and focuses on your background, experience, and understanding of data engineering concepts. Expect to discuss your familiarity with data platforms, SQL, and any relevant projects you've worked on. The recruiter will also gauge your fit within Canva's culture and values.

2. Technical Assessment

Following the initial screening, candidates are usually required to complete a technical assessment. This may involve a take-home coding challenge that tests your skills in SQL and Python, as well as your ability to analyze data and build data pipelines. The assessment is designed to reflect real-world tasks you would encounter in the role, such as data extraction, transformation, and loading (ETL) processes.

3. Technical Interview

If you perform well in the technical assessment, the next step is a technical interview with a senior engineer or a member of the data engineering team. This interview typically lasts about an hour and may include coding exercises, system design questions, and discussions about your previous work experiences. You might be asked to implement a data structure or solve a problem related to data processing, so be prepared to demonstrate your problem-solving skills in a collaborative environment.

4. Behavioral Interview

The behavioral interview is often the final stage of the process. This round usually involves a conversation with the hiring manager or a senior leader in the data team. Expect questions that explore your teamwork, communication skills, and how you handle challenges in a work setting. You may be asked to provide examples of how you've collaborated with cross-functional teams or managed stakeholder expectations in previous roles.

5. Final Review

In some cases, there may be a final review stage where you present a case study or discuss a project you’ve worked on that is relevant to the role. This could involve explaining your approach to a specific problem, the technologies you used, and the impact of your work. This stage is an opportunity to showcase your analytical thinking and ability to derive insights from data.

As you prepare for your interview, keep in mind the types of questions that may arise in each of these stages.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at Canva typically consists of multiple rounds, including a recruiter screening, technical assessments, and behavioral interviews. Familiarize yourself with this structure and prepare accordingly. Expect to discuss your favorite products, features you've built, and how you would improve existing products. This will not only help you articulate your thoughts but also demonstrate your understanding of the role and the company’s mission.

Prepare for Technical Assessments

As a Data Engineer, you will likely face coding challenges and system design questions. Brush up on your SQL and Python skills, as these are crucial for the role. Practice implementing data structures and algorithms, as well as designing data pipelines. Familiarize yourself with tools like Snowflake and AWS, as well as concepts related to ETL processes. Consider using platforms like LeetCode to practice coding problems that reflect the types of questions you might encounter.

Showcase Your Problem-Solving Skills

During the interviews, you may be asked to solve real-world problems or case studies. Approach these questions methodically: clarify the requirements, outline your thought process, and communicate your reasoning as you work through the problem. This will not only demonstrate your technical skills but also your ability to think critically and collaborate effectively.

Emphasize Collaboration and Communication

Canva values teamwork and cross-functional collaboration. Be prepared to discuss how you have worked with product managers, technical program managers, and other stakeholders in the past. Highlight your experience in gathering requirements, understanding user needs, and delivering solutions that enhance user experience. This will show that you can thrive in Canva's collaborative environment.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your fit within Canva's culture. Reflect on your past experiences and be ready to share specific examples that demonstrate your problem-solving abilities, adaptability, and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the impact of your actions.

Stay Engaged and Ask Questions

During the interview, engage with your interviewers by asking insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you gauge if Canva is the right fit for you. Inquire about the challenges the team is currently facing and how you can contribute to overcoming them.

Manage Your Time Wisely

The interview process can be lengthy, with multiple rounds and take-home assignments. Be mindful of the time commitment required and plan your preparation accordingly. Allocate sufficient time for each task, especially for take-home challenges, as they can be time-consuming. Ensure you balance thoroughness with efficiency to avoid burnout.

Reflect on Your Experience

After each interview round, take a moment to reflect on what went well and what could be improved. This will help you adjust your approach for subsequent interviews and demonstrate your commitment to personal growth. If you receive feedback, use it constructively to enhance your performance in future rounds.

By following these tips and preparing thoroughly, you can approach your interview at Canva with confidence and clarity. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Canva. The interview process will likely assess your technical skills, problem-solving abilities, and understanding of data engineering principles, as well as your capacity to collaborate with cross-functional teams.

Technical Skills

1. Can you explain the differences between ETL and ELT processes?

Understanding the nuances between these two data processing methods is crucial for a Data Engineer, especially in a role focused on data pipelines.

How to Answer

Discuss the definitions of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), emphasizing the order of operations and the implications for data processing and storage.

Example

“ETL involves extracting data from source systems, transforming it into a suitable format, and then loading it into a data warehouse. In contrast, ELT extracts data and loads it into the warehouse first, allowing for transformation to occur within the data warehouse itself. This can lead to faster data availability for analysis, especially with modern cloud data platforms.”

2. Describe your experience with SQL and how you have used it in your previous projects.

SQL is a fundamental skill for data engineers, and your ability to manipulate and query data effectively is essential.

How to Answer

Provide specific examples of how you have used SQL in your past roles, including any complex queries or optimizations you implemented.

Example

“In my previous role, I used SQL extensively to create complex queries for data extraction and reporting. For instance, I optimized a query that aggregated sales data across multiple regions, reducing the execution time from several minutes to under 30 seconds by indexing key columns and restructuring the query logic.”

3. How would you design a data pipeline to ingest data from a MySQL database into a Snowflake data warehouse?

This question tests your practical knowledge of data pipeline design and your familiarity with specific technologies.

How to Answer

Outline the steps you would take to design the pipeline, including data extraction, transformation, and loading processes, as well as any tools you would use.

Example

“I would start by using a tool like Fivetran to automate the extraction of data from the MySQL database. After extraction, I would transform the data using DBT to ensure it meets the schema requirements of Snowflake. Finally, I would load the transformed data into Snowflake, ensuring that the pipeline is scheduled to run at regular intervals to keep the data up to date.”

4. What strategies do you use to ensure data quality in your pipelines?

Data quality is critical in data engineering, and interviewers will want to know how you maintain it.

How to Answer

Discuss specific techniques you employ to validate and clean data, as well as any monitoring tools you use.

Example

“I implement data validation checks at various stages of the pipeline, such as schema validation and data type checks. Additionally, I use tools like Great Expectations to automate data quality testing and monitor data integrity over time. This proactive approach helps catch issues early and ensures that the data remains reliable for analysis.”

Problem-Solving and Design

5. Describe a challenging data engineering problem you faced and how you resolved it.

This question assesses your problem-solving skills and ability to handle real-world challenges.

How to Answer

Share a specific example of a problem you encountered, the steps you took to resolve it, and the outcome.

Example

“In a previous project, we faced performance issues with our data pipeline due to a sudden increase in data volume. I analyzed the bottlenecks and discovered that our transformation logic was inefficient. I refactored the code to use batch processing instead of row-by-row processing, which improved the pipeline’s performance by over 50%.”

6. How do you approach designing a data model for a new application?

This question evaluates your understanding of data modeling principles and your ability to design scalable solutions.

How to Answer

Discuss the steps you take to gather requirements, design the model, and ensure it meets the application’s needs.

Example

“I start by collaborating with stakeholders to gather requirements and understand the data needs of the application. Then, I create an entity-relationship diagram to visualize the data model, ensuring normalization to reduce redundancy. Finally, I validate the model with the team and iterate based on feedback before implementation.”

Collaboration and Communication

7. How do you ensure effective communication with cross-functional teams?

Collaboration is key in data engineering, and your ability to communicate effectively with other teams is essential.

How to Answer

Share your strategies for maintaining clear communication and collaboration with different stakeholders.

Example

“I prioritize regular check-ins with cross-functional teams to discuss project updates and gather feedback. I also use collaborative tools like Jira and Confluence to document progress and share insights, ensuring everyone is aligned and informed throughout the project lifecycle.”

8. Can you give an example of how you have influenced a product decision through data insights?

This question assesses your ability to leverage data to drive business decisions.

How to Answer

Provide a specific example of a time when your data analysis led to a significant product decision.

Example

“During a project, I analyzed user engagement data and discovered that a particular feature was underutilized. I presented my findings to the product team, suggesting enhancements based on user feedback. This led to a redesign of the feature, which ultimately increased user engagement by 30% after implementation.”