Google Data Analyst Interview Questions + Guide in 2024

According to Google’s latest news updates, it will continue to develop its products and cloud platform in 2024 while prioritizing AI. This means that Google will increasingly rely on its data analysts to generate insights from user data to inform its business strategies.

Google is known for its dynamic work culture, highly competitive salaries, emphasis on work-life harmony, and comprehensive health benefits.

This interview guide will walk you through the interview process with our selected Google data analyst interview questions, strategies, and interview tips. By the end of the article, you’ll have a clearer picture of the interview rounds, interviewers’ favorite questions, and how you should tackle them.

Unlike other companies, Google emphasizes probability and statistics in its hiring process. These concepts are foundational in analyzing user behavior and interpreting A/B tests and in more advanced machine learning applications. Apart from these skills, you can expect a lot of questions regarding SQL, Python, and product sense, as well as those assessing your cultural fit.

Please note that the interview questions will differ based on the team and function advertised in the job description. Always review the job description carefully when preparing your interview strategy.

Step 1: Initial Phone Call

Here, the interviewer (usually a hiring manager) will ask you exploratory questions to learn more about you and your interests, past projects, and skillsets that relate to the job role. The interviewer will also tell you about Google, its culture, the team you are applying for, and the scope of the role.

Make sure you prepare some responses for common behavioral and CV-based questions to ace this step. You can also use the opportunity to learn more about the next stages of the interview.

Step 2: Onsite Interviews

Google’s onsite interview consists of three to four rounds lasting approximately 45 minutes each— one with a hiring manager, a team manager, and a developer. Each interviewer will assess different aspects of your personality, such as:

• Cognitive Ability: This area is graded based on your critical thinking and ability to communicate ideas.
• Leadership: This will focus on whether you can help lead a group to a positive outcome.
• Role-related expertise: You’ll be given scenario-based questions that test your grasp of statistical concepts, coding skills, and product sense.
• Googliness: Google looks for “Googliness,” a term it defines as “a mashup of passion and drive that’s hard to define but easy to spot.” The company looks for “intrapreneurs,” people who have humility, curiosity, conscientiousness, and a track record of doing interesting things.

Read on to learn about our selection of the top behavioral and technical questions you can expect at your Google interview.

1. How will your previous work experience translate to this role?

This question will gauge how your past work and skills will help you shine in the role you’re applying for.

How to Answer

Focus on your analytical skills, proficiency with relevant tools and technologies, and project management experience. Read the job description well while preparing the answer to this question so that you can align your responses strategically.

Example

“In my previous role, I worked as a marketing analyst at a digital agency. I regularly analyzed campaign performance data to optimize strategies, using SQL and Python to pull and wrangle data. I believe that my ability to derive insights from complex data would help me contribute to enhancing user engagement strategies in the team.”

2. Why do you want to join Google?

Understanding why you want to join will help your interviewer determine if your values and aspirations align with Google’s mission.

How to Answer

Your answer should reflect your understanding of Google’s work, culture, and the specific opportunities that attract you to the company. Be honest and specific about how Google’s offerings align with your career goals.

Example

“I am inspired by Google’s commitment to innovation. Google’s approach to solving complex problems for its users aligns with my desire to contribute to meaningful projects with a global impact. I see a unique opportunity to use my analytical skills to help enhance product features and make a real difference in how people access and use information.”

3. Tell me about a time you failed.

One of Google’s values is “failing forward”—learning from mistakes is essential for Googlers to stay at the cutting edge of innovation.

How to Answer

Select a work-related example where you faced a setback and focus on what you learned from the experience. To structure your response in an organized manner, familiarize yourself with the STAR (situation, task, action, result) method.

Example

“In my previous role, I had to optimize a complex ETL process that was taking too long to complete. Based on my analysis, I proposed a series of performance improvements. However, when I implemented them, the process crashed, causing a significant data outage. It was a critical failure, and I worked tirelessly with the team to resolve the issue.

This experience taught me the importance of thorough testing and monitoring during any system optimization. I also learned the value of communication with the team and stakeholders, keeping them informed of progress and setbacks.”

4. What are your three most significant strengths and weaknesses?

This question is common in interviews because it helps the company gauge your self-awareness and honesty.

How to Answer

Select strengths that are relevant to the role and weaknesses that are genuine but not crippling to your performance. Also, discuss how you’re working to overcome your weaknesses.

Example

Strengths: “One of my greatest strengths is communication, especially in translating complex data insights into understandable terms for non-technical stakeholders. Another strength is my adaptability; working in fast-paced environments, I’ve learned to quickly adjust to new tools and technologies. Lastly, I’m highly collaborative. I believe in the power of teamwork and have consistently been able to bridge gaps between different departments.”

Weaknesses: “A weakness I’ve identified is my hesitation with public speaking. To improve, I’ve started attending workshops and actively seek opportunities to present in meetings.”

5. Tell me about a time when you had to take the lead in a challenging situation.

Leadership qualities are highly valued since they give employers an idea of whether you can take initiative, especially in a high-stakes situation.

How to Answer

Describe the context, the challenge, your response, and the outcome following the STAR framework. Highlight how you motivated the team and any critical decisions you made.

Example

“In my previous role, when our team was facing a critical deadline for launching a new analytics dashboard, the project lead unexpectedly had to take leave. I decided to coordinate the project’s final stages. I began by reassessing our priorities and redistributing tasks based on team members’ workloads. To address morale and ensure everyone felt supported, I initiated daily check-ins as a space for the team to voice concerns and progress updates. We successfully met the deadline, and the dashboard received positive feedback for its functionality and user interface.”

6. Given that X and Y are independent random variables with normal distributions, what is the mean and variance of the distribution of 2X−Y when the corresponding distributions are X∼N(3,4) and Y∼N(1,4)?

This question gauges your understanding of basic statistics.

How to Answer

To tackle such questions, clearly state your approach before diving into the solution.

Example

“The mean of 2X −Y would be 2∗3−1=5. The variance, since X and Y are independent, would be calculated as 2^2∗4+(−1)^2∗4=16+4=20. So, the distribution of 2X − Y is N(5,20).”

7. You are given data on user search query volumes on Google Search. How would you determine if there’s a statistically significant difference in the number of searches for Android vs iOS devices during the holiday season?

Understanding search trends informs targeted advertising campaigns or feature development for Android and iOS platforms.

How to Answer

Opt for a hypothesis testing framework using a two-sample t-test, assuming independent samples. Ensure the data is normally distributed or large enough for the central limit theorem to apply, and check that variances are comparable or use a version of the test that adjusts for unequal variances.

Example

“I’d aggregate the search queries for ‘Android’ and ‘iOS’ separately, focusing on the holiday season’s time frame. Then, assuming we have a sizable number of data points to satisfy normality assumptions, I’d conduct a two-sample t-test to compare the means of the two groups. The null hypothesis would be that there’s no difference in search volumes between Android and iOS devices during the holiday season. A significantly low p-value from the test would indicate that we can reject the null hypothesis.”

8. Given two sorted lists, write a function to merge them into one sorted list. What’s the time complexity?

This tests your ability to efficiently manipulate datasets. At Google, you’ll need to consolidate data from different sources, like user feedback from various platforms, into a single, organized dataset for analysis.

How to Answer

Implement a two-pointer technique to iterate through both lists simultaneously, comparing elements and adding the smaller one to a new list until you’ve gone through both lists. This minimizes the time and space needed to achieve a fully merged list.

Example

“I’d initialize two pointers at the start of each list. Comparing the elements at these pointers, I’d then add the smaller of the two to a new list and advance the pointer. This process repeats until all elements from both lists are in the new list. If one list is finished first, I’d append the rest of the other list directly. This method ensures a sorted merge and operates with a time complexity of O(n + m), where n and m are the lengths of the two lists.”

9. Choose a Google product you frequently use and propose a new feature to enhance user experience.

This question simultaneously assesses your understanding of user needs and familiarity with Google’s products.

How to Answer

Focus on a common problem or opportunity for enhancement within a product. Share your thought process as you define the scope of the problem and explain the solution.

Example

“For Google Docs, I often collaborate across different time zones, which can make live collaboration challenging. A feature I’d propose is ‘Collaborative Highlights,’ where users can highlight text or sections they have questions about or need revisions on, even when other collaborators are offline. Each highlight could include a timestamp and a short note for context. This would streamline the review process and make it easier for collaborators to see what needs their attention at a glance.”

10. Let’s say you’re working with survey data sent as multiple-choice questions. How would you test if survey responses were randomly filled out by individuals or if the answers were truthful?

In a Google context, ensuring the integrity of survey data would positively impact strategic decision-making, resulting in enhanced user experiences.

How to Answer

Discuss the statistical tests you would use to compare survey data against a uniform distribution expected from random selections. Mention the importance of understanding the survey’s context and expected response patterns to select the appropriate test.

Example

“I would first analyze the distribution of answers for each multiple-choice question. If responses were random, we’d expect a relatively uniform distribution across all options. To test this, I would use a chi-square test comparing the observed distribution of responses to the expected uniform distribution. Additionally, analyzing the pattern of responses across questions for each respondent might reveal anomalies, such as selecting the same option for all questions.”

11. We are considering two different layouts for the Google News homepage. How would you determine which layout leads to higher user engagement?

You will need to thoroughly understand A/B tests and ways to interpret them, as apps like Google News frequently test designs to see which ones users engage with more.

How to Answer

Apart from the details of the experiment, you should mention the importance of setting the right metrics for user engagement, such as click-through rates or time spent on the page.

Example

“Before beginning the experiment, I would define key engagement metrics, such as average session duration, click-through rate, and the number of articles read per session. I’d then randomly assign users to one of the two layouts to ensure unbiased results. After collecting data over a few days, I’d use t-tests to compare engagement metrics.”

12. Why would the same machine learning algorithm generate different success rates using the same dataset?

In a company like Google, which relies heavily on machine learning for its sophisticated search algorithms, having a deeper understanding of models is crucial.

How to Answer

Select a few applicable algorithms in the context of the team you’re applying for. Discuss key factors in these models and explain your answers, keeping relevant business scenarios in mind.

Example

“Algorithms like decision trees, random forests, and gradient boosting machines are sensitive to the specifics of their initialization settings and data splits due to their inherent randomness. For instance, decision trees can produce vastly different structures with slight changes in data due to their inherent nature of selecting splits that maximize information gain. Random forests introduce additional randomness by selecting random subsets of features for each tree. Gradient boosting machines, while robust, can also vary based on the subsampling process if stochastic gradient boosting is used.

If one were enhancing search algorithms, this variability would impact the reliability of search results. Therefore, it’s important to conduct cross-validation, use ensemble techniques, and carefully tune the initialization parameters and training procedures.”

13. You’re tasked with creating a dashboard for Google’s senior management team to track the performance of Google Cloud services across different regions. What KPIs would you include?

With this question, the interviewer will evaluate if you can identify important metrics for high-level decision-making.

How to Answer

It’s essential to select metrics that provide a comprehensive view of performance, are directly actionable, and align with the company’s strategic goals.

Example

“I’d include KPIs such as average response time and uptime percentage to monitor system reliability and performance. Customer metrics like net promoter score (NPS) and churn rate would offer insights into user satisfaction. From a financial perspective, revenue growth rate and cost of goods sold (COGS) would help assess economic health. Additionally, the dashboard should track the user growth rate and new accounts activation rate to track market penetration in each region.”

14. You are given two tables: employees and departments. Write a query to get the top 3 highest employee salaries by department.

This SQL question reflects real-world scenarios at Google where segmenting departmental performance based on specific criteria is needed for resource allocation.

How to Answer

Discuss using SQL window functions partitioned by department to rank salaries. Mention the importance of using conditional logic to filter the results.

Example

“I’d use the ROW_NUMBER() window function, partitioning by the department to assign a rank to each employee. The query would include a conditional statement to ensure that only the available top salaries are displayed for departments with less than three employees. The final step would involve filtering the results to show only those employees who rank in the top three.”

15. You have a dataset containing timestamps of when users watch YouTube videos and a separate dataset containing video advertisement timestamps. How would you process these datasets in Python to determine the average viewing duration before a user encounters an ad?

Google data analysts are expected to have at least a working knowledge of Python. This question tests your knowledge of Python libraries and functions in the context of data pre-processing.

How to Answer

Mention the importance of merging the two datasets on a common key and sorting the events by timestamp. Discuss which libraries and functions you would use.

Example

“I’d start by loading the datasets using pandas.read_csv() and convert timestamps using pandas.to_datetime(). After ensuring the datasets are properly formatted, I’d merge them using DataFrame.merge() on ‘user_id’ and ‘video_id.’ The next step would involve calculating the time difference between each video watch timestamp and the subsequent ad timestamp, using diff(). I would derive the average viewing duration before encountering an ad by grouping these differences by user session and calculating the average.”

16. You are a data scientist at YouTube. A PM comes to you worried that amateur video creators could do well before, but now it seems only “superstars” do well. How would you decide if this is true or not?

Understanding the dynamics of content creator success is essential for maintaining an ecosystem that supports both emerging and established creators.

How to Answer

Suggest a comparative analysis over time, focusing on metrics like view counts, subscriber growth, engagement rates (likes, comments), and monetization figures. Discuss the statistical tests you would use to determine if there are indeed any significant shifts.

Example

“I’d first define the criteria for what constitutes ‘amateur’ vs ‘superstar’ video creators, based on subscriber counts or average views. Using historical data, I’d analyze trends in key success metrics for both groups over several years. I’d also assess the significance of the changes using t-tests or ANOVA.”

17. You are given two tables: Subscriptions with columns UserID, SubscriptionStart, SubscriptionEnd, and SubscriptionType and Activity with columns UserID, ActivityDate, and ActivityType. Write an SQL query to find the average number of activities per user for each subscription type, only including activities that occurred during their subscription period.

This question gauges your ability to perform complex SQL queries involving joins, date comparisons, and aggregate functions.

How to Answer

Outline a strategy that involves joining the tables on UserID, filtering activities to only those that fall within the subscription period, and then grouping the results by SubscriptionType to calculate the average number of activities per user.

Example

“I’d join the tables on UserID, as this would allow us to align each user’s activities with their respective subscription periods. Next, I’d filter these joined results to include only the records where the ActivityDate falls within the SubscriptionStart and SubscriptionEnd dates.

After filtering, the data would be grouped by SubscriptionType, and for each group, I’d calculate the average number of activities using the AVG() function.”

18. Given three random variables independently and identically distributed from a uniform distribution of 0 to 4, what is the probability that their median is greater than 3?

Understanding distributions is pivotal in optimizing the performance of Google Ads, for example, by predicting click-through rates based on historical data.

How to Answer

Focus on identifying the probability of a single event and then extend this to calculate the combined probability that meets the specified condition.

Example

“Given the uniform distribution from 0 to 4, the probability of a single event is ¹⁄₄. To find the probability that the median of these variables is greater than 3, we must consider that at least two variables must be greater than 3.

The combined probability of these scenarios gives us the solution. The total probability of the median of three such variables being greater than 3 is approximately 0.15625.”

19. We’d like to predict the future demand for Google Cloud services among small and medium-sized enterprises (SMEs). What kind of data would you gather, and what predictive modeling techniques would you use?

Optimizing resource allocation while improving service offerings are critical tasks that Google expects its data analysts to assist with.

How to Answer

Discuss the importance of gathering both historical internal data and external market data. Mention predictive modeling techniques effective in time series forecasting, such as ARIMA for linear trends, and machine learning approaches like random forest or gradient boosting machines for capturing complex patterns.

Example

“I’d start by collecting data on historical usage of Cloud services by SMEs, like monthly active users, service tier changes, and usage intensity of different services. External market data such as SME growth rates and industry-specific digital transformation trends would also be vital to understand demand.”

20. Let’s say your team runs a standard control and variant A/B test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value. How would you assess the validity of the result?

The ability to evaluate these tests ensures that any feature changes are genuinely impactful before wider implementation.

How to Answer

Highlight potential pitfalls like multiple testing issues or data quality that could affect the validity of the result.

Example

“The result suggests a statistically significant difference between the control and variant, assuming our significance level is set at 0.05. However, to fully assess the result’s validity, I’d first ensure the test had sufficient power, typically 80% or higher, which might require a large enough sample size to detect a meaningful difference. I’d review the experimental design to confirm it was free of biases. It’s also important to consider the effect size, which tells us how impactful the change is in practical terms. Even if the result is statistically significant, a small effect size may not be significant to the business. Lastly, if multiple comparisons were made during the analysis, I’d apply a correction like Bonferroni to adjust the significance level and reduce the risk of Type I errors.”

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

Study the Company and Role

Research recent news, updates, Google values, and the company’s business challenges. Understanding the company’s culture and strategic goals will equip you to present yourself better and know if they are a good fit for you.

Once you learn more about the company, try to understand how the specific team you are applying to supports the company’s goals.

Visit Google’s careers page for more details about its hiring process. Also, check out this article on Googliness to see what Google looks for in an ideal employee.

Brush Up on Technical Skills

Gain proficiency in SQL, Python, and BI tools, as well as statistics, product sense, Excel, and metric development. Practice SQL problems that include window functions, complex joins, subqueries, lead and lag functions, etc. Remember to brush up on your knowledge of statistical and probability concepts, including regression, hypothesis testing, maximum likelihood estimation, and sampling.

A great way to boost your confidence is to work on projects that mimic real-world analytics challenges. Check our article on our handpicked data analytics projects.

Check out the resources we’ve compiled for data analysts: linear regression interview questions, statistics and A/B testing interview questions, and Google SQL interview questions.

If you need further guidance, you can explore our tailored learning paths on SQL, statistics and A/B testing, and data analytics.

Practice Problem-Solving and Behavioral Questions

Prepare for behavioral questions using the STAR method. Reflect on your past experiences and practice articulating them in a concise, impactful manner.

Visit our Interview Questions section to familiarize yourself with behavioral questions. It offers a wide range of practice questions to help structure your responses effectively using the STAR method.

To test your current preparedness for the interview process and improve your communication skills, try a mock interview.

Network with Employees

Connect with Google employees through LinkedIn or other online platforms. They can provide valuable insights into the company culture and the interview process.

Ask the Interviewer Relevant Questions

Have all your questions written down and ready for your interviewer. This will demonstrate your interest in the role and the company and give you valuable insights into what it’s like to work at Google.

If you feel this isn’t enough, read our guide on how to prepare for an interview as a data analyst for more information.

What is the average salary for a data analyst role at Google?

The average base salary for a data analyst at Google is $132,569, making the remuneration highly attractive for prospective applicants.

Check out our comprehensive Data Analyst Salary Guide for more insights into the salary range of data analysts at various companies, organized by city, seniority, and company.

Where can I read more discussion posts on the Google data analyst role here on Interview Query?

Go over to our discussion board, where our members talk about their Google interview experience. You can also use the search bar to look up analyst interview experiences to gain insights into comparable tech companies’ interview patterns.

Are there job postings for Google data analyst roles on Interview Query?

You can search our job portal for openings at Google or other MAANG companies. You can also make selections based on your preferred location, role, and level of seniority.

In conclusion, succeeding in Google data analyst interview questions requires a strong foundation in technical skills and problem-solving and the ability to work in an innovative and collaborative environment.

If you’re considering opportunities at other tech companies, check out our Company Interview Guides. We cover a range of companies, including Microsoft, IBM, Apple, and more.

For other data-related roles at Google, consider exploring our guides for business analyst, engineer, scientist, and other positions in our main Google interview guide.

You can also read through our other data analyst interview guides, such as our main guide, behavioral, SQL, Excel, and case studies, for more practice.

Understanding Google’s culture of innovation and collaboration and preparing thoroughly with both technical and behavioral questions is the key to your success.

Check out more of Interview Query’s content, and we hope you land your dream role at Google soon!