Waymo Business Intelligence Interview Questions + Guide in 2025

Waymo is an autonomous driving technology company with a mission to be the most trusted driver, focusing on building the world's most experienced self-driving vehicle to enhance mobility and safety.

The Business Intelligence role at Waymo involves developing and maintaining sophisticated reporting infrastructures critical for decision-making in the commercialization of autonomous driving technology. This position requires designing high-quality and scalable data pipelines with precise definitions, mappings, and transformations to ensure the consistency of metrics across the organization. You will be collaborating with various departments such as Finance, Operations, and Global Places and Services to facilitate accurate reporting and insights. Key responsibilities also include enriching Waymo's owned data with third-party information, constructing a self-service data function to promote data literacy among users, and maintaining financial systems for optimal performance.

Ideal candidates will possess over eight years of relevant experience, expertise in data infrastructure, and proficiency in tools like Big Query and GCP. Strong coding skills, particularly in SQL and Python, along with experience in data visualization (preferably Looker), are essential. A deep understanding of finance systems and datasets, along with preferred qualifications such as CPA certification and experience with GenAI, will set you apart.

This guide will help you prepare effectively for the interview process by providing insights into the role's expectations, the skills you need to showcase, and an understanding of how to align with Waymo's values and operational goals.

The interview process for the Business Intelligence role at Waymo is structured and involves multiple stages designed to assess both technical and interpersonal skills.

1. Initial Recruiter Call

The process typically begins with a brief phone call with a recruiter. This initial conversation lasts around 30-45 minutes and focuses on your background, experience, and motivation for applying to Waymo. The recruiter will also provide an overview of the role and the company, ensuring you have a clear understanding of what to expect.

2. Technical Screening

Following the recruiter call, candidates usually undergo one or two technical screening interviews. These interviews are often conducted virtually and may include coding challenges, SQL assessments, and discussions around data infrastructure and reporting skills. Expect to demonstrate your proficiency in SQL, Python, and data visualization tools, as well as your ability to solve real-world business problems using data.

3. Behavioral Interviews

Candidates will then participate in behavioral interviews, which may involve multiple interviewers. These sessions focus on assessing your fit within the company culture and your ability to collaborate with cross-functional teams. Expect questions that explore your past experiences, problem-solving approaches, and how you handle challenges in a team setting.

4. Onsite Interviews

The final stage typically consists of an onsite interview, which may be conducted virtually or in person. This stage usually includes several rounds of interviews with different team members, including technical assessments, case studies, and discussions about your previous work. You may be asked to present a project or a case study relevant to the role, showcasing your analytical and communication skills.

5. Final Review and Offer

After the onsite interviews, the hiring committee will review your performance across all stages. This process can take some time, and candidates may experience delays in receiving feedback. If successful, you will receive an offer that includes details about salary, benefits, and other employment terms.

As you prepare for your interview, it's essential to be ready for a mix of technical and behavioral questions that reflect the skills and experiences outlined in the job description.

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

Understand the Role and Its Impact

Before your interview, take the time to deeply understand the Business Intelligence role at Waymo and how it contributes to the company's mission of becoming the most trusted driver. Familiarize yourself with the specific responsibilities, such as developing reporting infrastructure and writing scalable data pipelines. This knowledge will allow you to articulate how your skills and experiences align with the company's goals and demonstrate your genuine interest in the position.

Prepare for Technical Proficiency

Given the technical nature of the role, ensure you are well-versed in SQL, Python, and data visualization tools like Looker. Review common data manipulation techniques and be prepared to discuss your experience with Big Query, Data Fusion, and GCP Infrastructure. Practice coding challenges that focus on data pipelines and reporting, as technical interviews often include practical assessments of your coding skills.

Showcase Your Problem-Solving Skills

During the interview, you may encounter case questions that require you to think critically about real-world scenarios. Prepare to discuss how you would approach specific business problems, such as enriching data sets with third-party data or maintaining financial systems. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly convey your thought process and the impact of your solutions.

Emphasize Collaboration and Communication

Waymo values cross-functional collaboration, so be ready to discuss your experience working with diverse teams, including finance, operations, and legal. Highlight instances where you successfully communicated complex data insights to non-technical stakeholders. This will demonstrate your ability to bridge the gap between technical and business perspectives, a crucial skill for the role.

Be Prepared for Behavioral Questions

Expect behavioral questions that assess your fit within Waymo's culture. Reflect on your past experiences and be ready to discuss challenges you've faced, how you handled them, and what you learned. Given the feedback from previous candidates, it’s important to convey your adaptability and openness to feedback, as these traits align with Waymo's values of being data-driven and open-minded.

Follow Up Professionally

After your interview, send a thoughtful thank-you email to your interviewers, expressing your appreciation for the opportunity to learn more about Waymo and the role. This not only reinforces your interest but also helps you stand out in a competitive hiring process. Given the feedback about communication issues during the hiring process, a polite follow-up can also serve as a gentle reminder of your candidacy.

By preparing thoroughly and approaching the interview with confidence and clarity, you can position yourself as a strong candidate for the Business Intelligence role at Waymo. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Business Intelligence interview at Waymo. Candidates should focus on demonstrating their technical skills, problem-solving abilities, and understanding of data infrastructure and reporting processes. Be prepared to discuss your experience with data pipelines, SQL, and data visualization tools, as well as your ability to work cross-functionally with various teams.

Technical Skills

**1. Can you explain how you would design a data pipeline for a new reporting requirement?

This question assesses your understanding of data pipeline architecture and your ability to translate business needs into technical solutions.**

How to Answer

Discuss the steps you would take to gather requirements, design the pipeline, and ensure data quality and consistency. Mention any tools or technologies you would use.

Example

“I would start by gathering requirements from stakeholders to understand the specific metrics they need. Then, I would design a pipeline using BigQuery for data storage and transformation, ensuring that data is cleaned and validated at each stage. I would also implement monitoring to catch any issues early and ensure that the data is consistently accurate.”

**2. What are some best practices for writing SQL queries for large datasets?

This question evaluates your SQL skills and your ability to optimize queries for performance.**

How to Answer

Discuss techniques such as indexing, using appropriate joins, and avoiding subqueries when possible. Mention how you would test and optimize your queries.

Example

“When working with large datasets, I prioritize using indexed columns in my WHERE clauses to speed up query performance. I also prefer to use JOINs instead of subqueries, as they tend to be more efficient. After writing my queries, I always analyze the execution plan to identify any bottlenecks and optimize accordingly.”

**3. How would you approach data visualization for a non-technical audience?

This question tests your ability to communicate complex data insights in an accessible way.**

How to Answer

Explain how you would choose the right visualization tools and techniques to convey the message clearly. Discuss the importance of storytelling with data.

Example

“I would start by understanding the key insights that the audience needs to grasp. Then, I would use Looker to create visualizations that highlight those insights, ensuring that I use simple charts and graphs. I would also provide context and narrative around the data to help the audience understand its implications.”

Business Acumen

**4. Describe a time when you had to work with multiple teams to deliver a project. How did you ensure alignment?

This question assesses your collaboration skills and ability to manage cross-functional projects.**

How to Answer

Share a specific example that highlights your communication and project management skills. Discuss how you facilitated collaboration and resolved any conflicts.

Example

“In my previous role, I led a project that required input from finance, operations, and IT. I organized regular check-in meetings to ensure everyone was aligned on goals and timelines. When conflicts arose, I facilitated discussions to find common ground and ensure that all perspectives were considered, which ultimately led to a successful project delivery.”

**5. What metrics would you consider when evaluating the performance of Waymo's autonomous fleet?

This question gauges your understanding of the business and the metrics that matter in the context of autonomous driving.**

How to Answer

Discuss relevant metrics such as safety, efficiency, and customer satisfaction. Explain how these metrics can inform decision-making.

Example

“I would focus on metrics like the number of safe miles driven, the average response time to incidents, and customer satisfaction scores. These metrics provide insights into both the operational efficiency of the fleet and the user experience, which are critical for Waymo’s success in the market.”

Problem-Solving

**6. How would you handle a situation where the data you received was inconsistent or incomplete?

This question tests your problem-solving skills and your approach to data quality issues.**

How to Answer

Explain your process for identifying data quality issues and the steps you would take to resolve them.

Example

“I would first conduct a thorough analysis to identify the extent of the inconsistencies. Then, I would reach out to the data source owners to understand the root cause. If necessary, I would implement data validation checks in the pipeline to prevent similar issues in the future and ensure that stakeholders are aware of any limitations in the data.”

**7. Can you give an example of a challenging data analysis problem you solved?

This question assesses your analytical skills and your ability to tackle complex problems.**

How to Answer

Share a specific example that highlights your analytical thinking and the impact of your solution.

Example

“In a previous role, I was tasked with analyzing customer churn rates. I discovered that the data was segmented incorrectly, leading to misleading insights. I restructured the data and applied statistical methods to identify key factors contributing to churn. My analysis led to targeted retention strategies that reduced churn by 15% over the next quarter.”