Handshake Data Engineer Interview Questions + Guide in 2025

Handshake is the career platform for Gen Z, connecting millions of students with employers and resources to forge meaningful career opportunities.

The Data Engineer role at Handshake is pivotal in shaping the company’s data architecture and ensuring the seamless flow of information across its platforms. As a Data Engineer, you will be responsible for designing, building, and maintaining scalable and efficient data pipelines that support various cross-functional teams in their data-driven initiatives. Your work will help deliver timely and accurate data access, enabling better decision-making and innovative features that contribute to the company’s mission of removing barriers to career opportunities.

To excel in this role, you will need strong expertise in data engineering tools and technologies, alongside a deep understanding of cloud platforms, particularly Google Cloud Platform (GCP). Familiarity with large-scale data services, data warehousing, and best practices in data governance is crucial. Additionally, proficiency in programming languages like Python and SQL will be essential for implementing complex data transformations. Traits such as strategic problem-solving, effective collaboration with technical and non-technical stakeholders, and the ability to mentor and guide team members will make you a great fit for Handshake's inclusive and innovative team environment.

This guide will provide you with insights into the expectations and requirements for the Data Engineer role, allowing you to prepare effectively for your interview and showcase the skills and experiences that align with Handshake’s goals.

The interview process for a Data Engineer role at Handshake is structured to assess both technical skills and cultural fit, ensuring candidates align with the company's values and mission. The process typically consists of several key stages:

1. Initial Recruiter Call

The first step is a phone call with a recruiter, lasting about 30 minutes. This conversation focuses on your background, skills, and experiences relevant to the Data Engineer role. The recruiter will also provide insights into Handshake's culture and the specifics of the position, helping to gauge your fit within the organization.

2. Take-Home Technical Assignment

Following the initial call, candidates are given a take-home technical assignment. This task allows you to demonstrate your data engineering skills in a practical context. You can choose to complete this assignment at home or allocate a portion of your onsite interview time to work on it. The assignment typically involves designing data pipelines or solving data-related problems, showcasing your approach to real-world scenarios.

3. Technical Phone Screen

The next step is a technical phone screen, where you will engage with a hiring manager or a senior data engineer. This interview focuses on your technical knowledge and problem-solving abilities. Expect questions related to data engineering concepts, such as data pipeline architecture, ETL processes, and specific technologies relevant to the role. This stage is crucial for assessing your technical depth and understanding of data systems.

4. Onsite Interview

The final stage is an onsite interview, which lasts approximately four hours. This comprehensive session includes multiple components: a coding interview, a systems design interview, and discussions with cross-functional team members. You may also meet with a skip-level manager to discuss your career aspirations and how they align with Handshake's goals. The coding interview will test your programming skills, while the systems design interview will evaluate your ability to architect scalable and efficient data solutions.

Throughout the interview process, candidates have noted the supportive and friendly demeanor of the interviewers, which helps create a comfortable environment for showcasing your skills and experiences.

As you prepare for your interview, consider 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 Process

The interview process at Handshake is structured and consists of multiple stages, including a recruiter call, a take-home technical assignment, a technical phone screen, and an onsite interview. Familiarize yourself with each stage and prepare accordingly. For the take-home assignment, focus on planning your approach to ensure your code is well-structured and documented. This will demonstrate your ability to think critically and design effective solutions.

Showcase Your Technical Expertise

As a Data Engineer, you will be expected to have a deep understanding of data architecture, cloud technologies, and data processing frameworks. Brush up on your knowledge of tools like Google Cloud Platform (GCP), BigQuery, and DataFlow. Be prepared to discuss your experience with building scalable data pipelines and your approach to data governance and compliance. Highlight specific projects where you successfully implemented data solutions that met business needs.

Emphasize Collaboration Skills

Handshake values cross-functional collaboration, so be ready to discuss how you have worked with product managers, engineering teams, and other stakeholders in the past. Share examples of how you have influenced decisions and driven projects forward through effective communication and teamwork. This will demonstrate your ability to engage with both technical and non-technical team members.

Prepare for Problem-Solving Questions

Expect to face questions that assess your problem-solving skills and strategic thinking. Practice articulating your thought process when tackling complex data challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly outline the context, your role, the actions you took, and the outcomes achieved.

Be Authentic and Personable

The interviewers at Handshake are known for being kind and supportive. Approach the interview with a positive attitude and be yourself. Share your passion for data engineering and how it aligns with Handshake's mission to create impactful products for students. Authenticity can help you connect with your interviewers and leave a lasting impression.

Ask Insightful Questions

Prepare thoughtful questions that reflect your interest in the role and the company. Inquire about the team dynamics, the challenges they face, and how the data engineering function contributes to Handshake's overall goals. This not only shows your enthusiasm but also helps you assess if the company culture aligns with your values.

Follow Up with Gratitude

After the interview, send a thank-you email to express your appreciation for the opportunity to interview. Mention specific points from the conversation that resonated with you, reinforcing your interest in the role and the company. This small gesture can set you apart from other candidates and leave a positive impression.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Handshake. The interview process will focus on your technical expertise, problem-solving abilities, and collaborative skills, as well as your understanding of data architecture and engineering principles. Be prepared to discuss your past experiences and how they relate to the responsibilities outlined in the role.

Data Engineering and Architecture

1. Can you describe a data pipeline you have built and the technologies you used?

This question assesses your practical experience in data engineering and your familiarity with various tools and technologies.

How to Answer

Discuss the specific technologies you used, the challenges you faced, and how you overcame them. Highlight the impact of the pipeline on the business or project.

Example

“I built a data pipeline using Apache Airflow and Google Cloud Dataflow to process real-time data from various sources. The pipeline transformed and loaded data into BigQuery for analytics. One challenge was ensuring data quality, which I addressed by implementing validation checks at each stage of the pipeline.”

2. What are the key differences between ETL and ELT processes?

Understanding the differences between these two data processing methods is crucial for a data engineer.

How to Answer

Explain the fundamental differences in terms of data flow, processing, and use cases. Provide examples of when you would use each approach.

Example

“ETL stands for Extract, Transform, Load, where data is transformed before loading into the target system. ELT, on the other hand, loads raw data first and then transforms it within the target system. I prefer ELT for large datasets in cloud environments, as it allows for more flexibility and scalability.”

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

This question evaluates your approach to maintaining high data quality standards.

How to Answer

Discuss the methods and tools you use to monitor data quality, such as validation checks, automated testing, and data profiling.

Example

“I implement data validation checks at each stage of the pipeline, using tools like Great Expectations for automated testing. Additionally, I regularly perform data profiling to identify anomalies and ensure data integrity.”

4. Can you explain the concept of data partitioning and its benefits?

Understanding data partitioning is essential for optimizing data storage and retrieval.

How to Answer

Define data partitioning and discuss its advantages, such as improved query performance and easier data management.

Example

“Data partitioning involves dividing a dataset into smaller, manageable pieces based on certain criteria, like date or region. This improves query performance by allowing the database to scan only relevant partitions, reducing processing time and costs.”

5. Describe your experience with cloud-based data technologies, particularly GCP.

This question assesses your familiarity with cloud platforms and their data services.

How to Answer

Share specific experiences with GCP services, such as BigQuery, Dataflow, or BigTable, and how you utilized them in your projects.

Example

“I have extensive experience with Google BigQuery for data warehousing and analytics. I used it to run complex queries on large datasets, leveraging its serverless architecture to scale efficiently without managing infrastructure.”

Performance and Optimization

1. How do you identify and resolve performance bottlenecks in data processing systems?

This question evaluates your analytical skills and problem-solving approach.

How to Answer

Discuss the tools and techniques you use to monitor performance and how you approach troubleshooting.

Example

“I use monitoring tools like Google Cloud Monitoring to track performance metrics. When I identify a bottleneck, I analyze query execution plans and optimize SQL queries or adjust resource allocation to improve performance.”

2. What strategies do you use to optimize data storage costs?

This question assesses your understanding of cost-effective data management.

How to Answer

Discuss techniques such as data compression, partitioning, and choosing the right storage solutions.

Example

“I optimize storage costs by implementing data lifecycle policies to archive or delete unused data. Additionally, I use data compression techniques to reduce storage size and leverage cheaper storage options for infrequently accessed data.”

3. Can you explain the difference between row-oriented and column-oriented storage?

Understanding storage formats is crucial for data retrieval and performance.

How to Answer

Define both storage types and discuss their use cases and performance implications.

Example

“Row-oriented storage is optimized for transactional workloads, where entire rows are read at once, making it suitable for OLTP systems. Column-oriented storage, on the other hand, is better for analytical queries, as it allows for faster access to specific columns, reducing I/O operations.”

4. How do you approach scaling data processing systems?

This question evaluates your strategic thinking regarding system architecture.

How to Answer

Discuss your experience with scaling techniques, such as horizontal scaling, load balancing, and distributed computing.

Example

“I approach scaling by implementing horizontal scaling techniques, adding more nodes to distribute the load. I also use load balancers to manage traffic and ensure that no single node becomes a bottleneck.”

5. Describe a time when you had to troubleshoot a data processing failure. What steps did you take?

This question assesses your problem-solving skills in real-world scenarios.

How to Answer

Outline the steps you took to identify the issue, the tools you used, and the resolution process.

Example

“When a data pipeline failed due to a schema change in the source data, I quickly identified the issue using logs and monitoring tools. I then updated the transformation logic to accommodate the new schema and implemented alerts to catch similar issues in the future.”

Collaboration and Communication

1. How do you collaborate with cross-functional teams to define data architecture requirements?

This question evaluates your teamwork and communication skills.

How to Answer

Discuss your approach to working with product managers and other stakeholders to gather requirements and ensure alignment.

Example

“I hold regular meetings with cross-functional teams to gather requirements and understand their data needs. I also create documentation to ensure everyone is aligned on the data architecture and any changes made during the development process.”

2. Can you provide an example of how you influenced a non-technical stakeholder regarding a data solution?

This question assesses your ability to communicate complex concepts to non-technical audiences.

How to Answer

Share a specific instance where you successfully communicated the value of a data solution to a non-technical stakeholder.

Example

“I presented a data analytics solution to the marketing team, explaining how it could enhance their targeting strategies. I used visualizations to demonstrate potential outcomes, which helped them understand the value and led to their support for the project.”

3. How do you handle conflicts or disagreements within a team?

This question evaluates your conflict resolution skills.

How to Answer

Discuss your approach to addressing conflicts, focusing on communication and collaboration.

Example

“When conflicts arise, I encourage open dialogue to understand different perspectives. I facilitate discussions to find common ground and work towards a solution that aligns with our goals.”

4. Describe a project where you had to mentor or guide junior engineers.

This question assesses your leadership and mentoring abilities.

How to Answer

Share your experience mentoring others, focusing on your approach and the outcomes.

Example

“I mentored a junior engineer on a data pipeline project, guiding them through best practices in data modeling and ETL processes. I provided regular feedback and encouraged them to take ownership of specific tasks, which helped them grow their skills and confidence.”

5. How do you ensure that your team stays updated with the latest data engineering trends and technologies?

This question evaluates your commitment to continuous learning and team development.

How to Answer

Discuss your strategies for fostering a culture of learning within your team.

Example

“I encourage my team to participate in workshops and conferences, and I organize regular knowledge-sharing sessions where we discuss new technologies and trends. I also provide access to online courses to help them stay current.”