Square Data Engineer Interview Questions + Guide in 2025

Square is a financial technology company dedicated to empowering businesses and individuals through accessible financial services.

As a Data Engineer at Square, you will play a pivotal role in designing and implementing robust data solutions that support the company’s suite of financial products aimed at helping sellers manage their business cash flow. You will work closely with cross-functional teams including product, engineering, data science, and machine learning to develop reliable datasets that enhance decision-making processes. Key responsibilities include building scalable data pipelines using Big Data technologies, promoting high-quality software engineering practices, and addressing data quality and integrity challenges. Ideal candidates should have significant experience in large-scale data processing, hands-on expertise with cloud-based data solutions, and a passion for analytics use cases and data modeling. Additionally, strong programming skills and a commitment to continuous learning in the evolving data landscape are essential traits that align with Square's innovative culture.

This guide will equip you with the insights needed to navigate the interview process effectively, highlighting the critical areas of focus and the skills that Square values most in candidates.

The interview process for a Data Engineer role at Square is structured to assess both technical skills and cultural fit within the team. It typically consists of several stages, each designed to evaluate different aspects of your qualifications and experiences.

1. Initial HR Screen

The process begins with a brief phone call with a recruiter, lasting around 15 to 30 minutes. During this initial screen, the recruiter will review your resume, discuss your background, and gauge your interest in the role. This is also an opportunity for you to ask questions about the company culture and the specifics of the position.

2. Hiring Manager Interview

Following the HR screen, candidates usually have a one-on-one interview with the hiring manager. This session typically lasts about 30 minutes and focuses on your past experiences, particularly in relation to data engineering. The hiring manager may delve into your understanding of data quality, security issues, and your approach to problem-solving in previous roles.

3. Technical Screen

The technical screening phase is crucial and generally consists of two parts: a coding assessment and a SQL interview. This stage can last up to an hour and is often conducted via a coding platform. You will be asked to solve problems related to Python and SQL, with a focus on medium-level complexity questions. Expect to discuss your thought process and approach to the problems before diving into coding.

4. Onsite Panel Interview

The final stage is an onsite panel interview, which can be extensive, often comprising multiple rounds. This may include discussions on data modeling, analytical thinking, and your past experiences. Each round typically lasts between 30 to 45 minutes, and you may encounter questions that assess your ability to work cross-functionally and your fit within the team. Candidates are usually required to sign a Non-Disclosure Agreement (NDA) before participating in this stage.

Throughout the interview process, Square places a strong emphasis on team dynamics and cultural fit, so be prepared to demonstrate your curiosity and adaptability to the role.

Now that you have an understanding of the interview process, let’s explore the specific questions that candidates have encountered during their interviews.

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

Understand the Interview Structure

The interview process at Square for a Data Engineer role typically includes multiple stages, starting with a recruiter phone screen, followed by a technical screen focusing on SQL and Python, and culminating in an on-site panel interview. Familiarize yourself with this structure and prepare accordingly. Knowing what to expect can help you manage your time and energy effectively throughout the process.

Prepare for Technical Proficiency

Given the emphasis on SQL and Python, ensure you are comfortable with medium-level coding challenges, particularly those involving window functions and data modeling. Practice coding on platforms like CoderPad, as you may be asked to solve problems in real-time. Additionally, brush up on your knowledge of data pipeline technologies such as Airflow and DBT, as well as cloud platforms like AWS or GCP, since these are crucial for the role.

Showcase Your Experience with Data Quality

Square places a strong emphasis on data quality and integrity. Be prepared to discuss your past experiences in addressing data quality issues, implementing data validation techniques, and ensuring the reliability of datasets. Highlight specific examples where you successfully improved data processes or resolved data-related challenges in your previous roles.

Emphasize Team Collaboration

The company values team building and cultural fit, so be ready to discuss how you have collaborated with cross-functional teams in the past. Share examples of how you have partnered with product managers, data scientists, and other stakeholders to understand their needs and deliver effective data solutions. Demonstrating your ability to work well in a team will resonate positively with the interviewers.

Be Curious and Adaptable

Square appreciates candidates who show curiosity and a willingness to learn. During your interviews, express your enthusiasm for emerging technologies and your commitment to professional development. Discuss any recent projects or learning experiences that showcase your adaptability and eagerness to stay current in the fast-evolving field of data engineering.

Prepare for Behavioral Questions

Expect behavioral questions that assess your problem-solving skills and analytical thinking. Use the STAR (Situation, Task, Action, Result) method to structure your responses. This will help you articulate your thought process clearly and demonstrate how you approach complex data problems.

Maintain a Positive Attitude

While the interview process may seem lengthy and rigorous, maintaining a positive attitude can make a significant difference. Approach each round with confidence and view it as an opportunity to learn more about the company and the role. Your demeanor can influence how interviewers perceive your fit within the team and company culture.

By following these tips and preparing thoroughly, you can position yourself as a strong candidate for the Data Engineer role at Square. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Square. The interview process will likely focus on your technical skills, experience with data processing, and your ability to work collaboratively with cross-functional teams. Be prepared to discuss your past projects, data modeling techniques, and how you ensure data quality and integrity.

Technical Skills

1. Can you explain the differences between SQL and NoSQL databases, and when you would use each?

Understanding the strengths and weaknesses of different database types is crucial for a Data Engineer.

How to Answer

Discuss the characteristics of SQL and NoSQL databases, including their structure, scalability, and use cases. Provide examples of scenarios where one might be preferred over the other.

Example

“SQL databases are structured and use a predefined schema, making them ideal for complex queries and transactions. In contrast, NoSQL databases are more flexible and can handle unstructured data, which is beneficial for applications requiring rapid scaling, such as real-time analytics.”

2. Describe your experience with ETL processes. What tools have you used?

ETL (Extract, Transform, Load) processes are fundamental to data engineering, and familiarity with various tools is essential.

How to Answer

Highlight specific ETL tools you have used, such as Airflow or DBT, and describe your role in designing and implementing ETL pipelines.

Example

“I have extensive experience with Airflow for orchestrating ETL workflows. In my previous role, I designed a pipeline that extracted data from multiple sources, transformed it for analysis, and loaded it into a data warehouse, ensuring data quality and integrity throughout the process.”

3. How do you ensure data quality and integrity in your projects?

Data quality is critical for reliable analytics and decision-making.

How to Answer

Discuss the methods you use to validate and cleanse data, as well as any tools or frameworks that assist in maintaining data integrity.

Example

“I implement data validation checks at various stages of the ETL process, using tools like Great Expectations to automate testing. Additionally, I regularly monitor data quality metrics and conduct audits to identify and rectify any discrepancies.”

4. Can you explain the Medallion architecture and its benefits?

Understanding modern data architecture is important for a Data Engineer at Square.

How to Answer

Describe the Medallion architecture's layers (Bronze, Silver, Gold) and how it facilitates data processing and analytics.

Example

“The Medallion architecture organizes data into three layers: Bronze for raw data, Silver for cleaned and enriched data, and Gold for aggregated data ready for analysis. This structure allows for efficient data processing and ensures that users can access high-quality datasets tailored to their needs.”

5. What programming languages are you proficient in, and how have you used them in data engineering?

Programming skills are essential for building data pipelines and automating processes.

How to Answer

Mention the programming languages you are familiar with and provide examples of how you have applied them in your work.

Example

“I am proficient in Python and Java. I primarily use Python for data manipulation and building ETL pipelines, leveraging libraries like Pandas and PySpark to process large datasets efficiently.”

Analytical Thinking

1. Describe a complex data problem you faced and how you solved it.

This question assesses your problem-solving skills and analytical thinking.

How to Answer

Provide a specific example of a challenging data issue, the steps you took to address it, and the outcome.

Example

“In a previous project, I encountered inconsistent data formats across multiple sources. I developed a data normalization process that standardized the formats before loading them into the data warehouse, which improved the accuracy of our analytics significantly.”

2. How do you approach designing a data pipeline?

Your approach to pipeline design reflects your understanding of data flow and architecture.

How to Answer

Discuss the key considerations you take into account when designing a data pipeline, such as scalability, reliability, and performance.

Example

“When designing a data pipeline, I start by understanding the data sources and the required transformations. I prioritize scalability by using distributed processing frameworks like Spark and ensure reliability by implementing monitoring and alerting mechanisms to catch any failures early.”

3. How do you stay updated with emerging technologies in data engineering?

Continuous learning is vital in the fast-evolving field of data engineering.

How to Answer

Share the resources you use to keep your skills current, such as online courses, webinars, or industry publications.

Example

“I regularly attend webinars and follow industry leaders on platforms like LinkedIn. I also participate in online courses to learn about new tools and technologies, ensuring that I can apply the latest best practices in my work.”

4. Can you give an example of how you collaborated with cross-functional teams?

Collaboration is key in a role that interfaces with various departments.

How to Answer

Describe a specific project where you worked with other teams, highlighting your role and contributions.

Example

“I collaborated with the data science team to understand their requirements for a new analytics dashboard. By gathering their input on the data needed and the desired outcomes, I was able to design a data pipeline that provided them with timely and accurate insights.”

5. What strategies do you use to optimize data pipelines?

Optimization is crucial for maintaining performance in data engineering.

How to Answer

Discuss specific techniques or tools you use to enhance the efficiency of data pipelines.

Example

“I focus on optimizing data pipelines by implementing partitioning and indexing strategies in the data warehouse. Additionally, I regularly review and refactor code to eliminate bottlenecks and improve processing times.”