WeWork Data Engineer Interview Questions + Guide in 2025

WeWork is a leading provider of flexible workspace solutions designed to help businesses thrive across the globe.

As a Data Engineer at WeWork, you will play a pivotal role in harnessing data to drive insights and enhance the customer experience. Your primary responsibilities will include designing and implementing efficient data solutions, managing cloud data warehouses, and collaborating with cross-functional teams to address complex business challenges. A successful candidate will have a strong foundation in data warehousing concepts and practices, alongside proficiency in SQL and experience with modern data engineering tools and technologies. You will also be expected to take initiative, foster collaboration, and contribute to a culture that values trust and innovation. Your ability to manage access control policies and develop self-service data pipelines will be crucial to ensuring data integrity and security at scale.

This guide will help you prepare effectively by providing insights into the expectations for the role and the types of questions you may encounter during the interview process.

The interview process for a Data Engineer position at WeWork is designed to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each focusing on different aspects of the candidate's qualifications and experiences.

1. Initial Recruiter Call

The process begins with an initial phone call with a recruiter, which usually lasts around 30 minutes. During this conversation, the recruiter will discuss the role, the company culture, and the candidate's background. This is an opportunity for the recruiter to gauge the candidate's interest in the position and to assess their communication skills and overall fit for WeWork.

2. Technical Screening

Following the initial call, candidates may undergo a technical screening, which can be conducted via video call. This interview typically lasts about 45 minutes and is led by a hiring manager or a senior data engineer. Candidates can expect to discuss their technical expertise, particularly in SQL and data warehousing concepts. They may be asked to solve a technical problem or to explain their approach to a data engineering challenge, such as writing SQL queries or discussing data pipeline architecture.

3. In-Depth Technical Interview

Candidates who successfully pass the technical screening will be invited to a more in-depth technical interview. This stage may involve multiple rounds, where candidates are assessed on their proficiency with data engineering tools and technologies, such as cloud data warehouses (e.g., Snowflake, BigQuery) and workflow management platforms (e.g., Airflow). Candidates should be prepared to demonstrate their problem-solving skills and to discuss their past experiences in detail, including any relevant projects they have worked on.

4. Behavioral Interview

In addition to technical assessments, candidates will also participate in a behavioral interview. This interview focuses on the candidate's soft skills, teamwork, and cultural fit within WeWork. Interviewers will explore how candidates have handled challenges in previous roles, their approach to collaboration, and their ability to communicate effectively with cross-functional teams.

5. Final Interview

The final stage of the interview process may involve a meeting with senior leadership or team members. This is an opportunity for candidates to ask questions about the company and the team they would be joining. It also allows the interviewers to assess the candidate's alignment with WeWork's values and mission.

As you prepare for your interview, it's essential to be ready for the specific questions that may arise during these stages.

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

Understand WeWork's Data-Driven Culture

WeWork places a strong emphasis on data as a core component of its business strategy. Familiarize yourself with how data influences decision-making at WeWork and be prepared to discuss how your experience aligns with this data-centric approach. Highlight specific instances where your data engineering work has directly impacted business outcomes or improved processes.

Prepare for Behavioral Questions

Expect behavioral questions that assess your communication skills, empathy, and initiative. WeWork values collaboration and a positive influence within teams, so be ready to share examples of how you've successfully worked with cross-functional teams, resolved conflicts, or contributed to a positive team culture. Use the STAR (Situation, Task, Action, Result) method to structure your responses effectively.

Brush Up on Technical Skills

Given the technical nature of the Data Engineer role, ensure you are well-versed in SQL and familiar with cloud data warehousing solutions like Snowflake or BigQuery. Be prepared to discuss your experience with data pipeline management, ETL processes, and performance tuning. Practice articulating your thought process when solving technical problems, as you may be asked to explain your approach verbally.

Be Ready for Technical Challenges

During the interview, you may face technical challenges that require you to demonstrate your problem-solving skills in real-time. Practice coding problems related to data manipulation and optimization, and be prepared to discuss your reasoning behind your solutions. If asked to solve a problem verbally, take your time to think through your approach and communicate clearly, as this will showcase your analytical thinking.

Showcase Your Passion for Data Engineering

WeWork is looking for candidates who are excited about building and maintaining a modern data stack. Share your enthusiasm for data engineering by discussing any personal projects, contributions to open-source, or continuous learning efforts in the field. This will help convey your commitment to the role and the industry.

Engage with Your Interviewers

Throughout the interview, engage with your interviewers by asking insightful questions about the team, projects, and challenges they face. This not only demonstrates your interest in the role but also allows you to assess if the company culture and team dynamics align with your values. Be sure to inquire about the tools and technologies they use, as well as opportunities for professional growth within the company.

Reflect on Feedback and Adapt

If you receive feedback during the interview, whether positive or constructive, take it in stride and show your willingness to learn and adapt. This reflects the growth mindset that WeWork values. If you encounter a challenging question or scenario, don’t hesitate to ask for clarification or express your thought process, as this can lead to a more productive discussion.

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

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at WeWork. The interview will likely focus on your technical skills, problem-solving abilities, and your experience with data engineering concepts and tools. Be prepared to discuss your past projects, your approach to data management, and how you collaborate with cross-functional teams.

Technical Skills

1. Can you explain the differences between a data warehouse and a data lake?

Understanding the distinctions between these two data storage solutions is crucial for a data engineer, especially in a company that relies heavily on data for decision-making.

How to Answer

Discuss the purpose of each storage solution, highlighting how data warehouses are optimized for structured data and analytics, while data lakes can handle unstructured data and are more flexible.

Example

“A data warehouse is designed for structured data and is optimized for query performance, making it ideal for analytics and reporting. In contrast, a data lake can store both structured and unstructured data, allowing for more flexibility in data processing and analysis, which is essential for exploratory data analysis and machine learning applications.”

2. Describe your experience with cloud data warehouses. Which platforms have you used?

This question assesses your familiarity with cloud technologies, which are essential for modern data engineering roles.

How to Answer

Mention specific platforms you have worked with, your role in managing them, and any relevant projects that demonstrate your expertise.

Example

“I have extensive experience with Snowflake and Google BigQuery. In my previous role, I was responsible for administrating our Snowflake instance, optimizing query performance, and implementing role-based access controls to ensure data security.”

3. How do you approach performance tuning in SQL?

Performance tuning is a critical skill for data engineers, as it directly impacts the efficiency of data processing.

How to Answer

Discuss specific techniques you use for performance tuning, such as indexing, query optimization, and analyzing execution plans.

Example

“I start by analyzing the execution plan of slow queries to identify bottlenecks. I often use indexing to speed up data retrieval and rewrite queries to minimize the number of joins or subqueries. For instance, I once improved a report generation query by 50% by restructuring it to use a more efficient join strategy.”

4. What is your experience with ETL processes? Can you describe a project where you implemented an ETL pipeline?

ETL (Extract, Transform, Load) processes are fundamental to data engineering, and this question evaluates your hands-on experience.

How to Answer

Provide details about the ETL tools you’ve used, the data sources involved, and the impact of the pipeline on the business.

Example

“I implemented an ETL pipeline using Apache Airflow to automate the data ingestion from various sources into our data warehouse. This pipeline transformed raw data into a structured format, enabling our analytics team to generate insights more quickly, which ultimately improved our decision-making process.”

Collaboration and Communication

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

Collaboration is key in data engineering, and this question assesses your interpersonal skills.

How to Answer

Discuss your strategies for maintaining clear communication, such as regular meetings, documentation, and using collaborative tools.

Example

“I prioritize regular check-ins with cross-functional teams to ensure alignment on project goals. I also maintain thorough documentation of our data models and processes, which helps facilitate discussions and ensures everyone is on the same page.”

6. Describe a time when you had to explain a complex technical concept to a non-technical audience.

This question evaluates your ability to communicate effectively with stakeholders who may not have a technical background.

How to Answer

Share a specific example where you simplified a technical concept and the methods you used to make it understandable.

Example

“I once had to explain our data pipeline architecture to the marketing team. I used visual aids and analogies to illustrate how data flows from various sources to our analytics platform, which helped them understand how their campaigns were being tracked and analyzed.”

Problem-Solving

7. Can you describe a challenging data engineering problem you faced and how you resolved it?

This question assesses your problem-solving skills and ability to handle challenges in data engineering.

How to Answer

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

Example

“I encountered a significant performance issue with our data ingestion process, which was causing delays in reporting. I conducted a thorough analysis and discovered that the bottleneck was due to inefficient data transformations. I restructured the ETL process to parallelize the transformations, which reduced the ingestion time by 70%.”

8. How do you stay updated with the latest trends and technologies in data engineering?

This question gauges your commitment to continuous learning and professional development.

How to Answer

Mention specific resources you use, such as online courses, blogs, or conferences, and how you apply new knowledge to your work.

Example

“I regularly follow industry blogs and participate in webinars to stay informed about the latest trends in data engineering. I also attend conferences like the Data Engineering Conference, where I can network with other professionals and learn about new tools and best practices.”