Google Data Analyst Interview Guide (2025): Process, Questions & Preparation
Introduction
While Google reported a record year-over-year revenue growth of 12 percent in Q4 2024, it has lost slight ground in the Google Search department. However, it has secured considerable traction in Google Cloud and AI Infrastructures. These now contribute over 12% of total revenue and have increased the importance of its data analyst and engineering roles. The Google Subscriptions, Platforms, and Devices segment also generated over 11.5% of total revenue for the quarter. Despite recent market shifts, the AI capabilities of Gemini 2.5 Pro and other integrations announced during I/O 2025 highlight exceptional AI operational efficiencies and strong profit generation.
Role Overview & Culture
In line with the strategic focus on AI capabilities, Google’s data analyst interview process has also become more rigorous, with the role now evolving to lean towards data science and strategic decision-making. As a data analyst at Google, you’ll be responsible for collecting, organizing, and analyzing data from GA, Google Ads, and a range of internal and external platforms, while involving yourself into the development and implementation of innovative data tracking strategies to ensure more precise and reliable data collection.
In a more day-to-day sense, depending on your seniority, you’ll be asked to build dashboards, run SQL, BigQuery queries, and partner with product managers of specialized teams to optimize products and campaigns. Your capacity to navigate ambiguity and drive solutions, combined with strong technical expertise, will play a pivotal role in your growth and impact in the position.
Why This Role at Google?
While working at Google is a perk in itself, with transparent career progression and professional development, the compensation for the data analyst role also deserves mention. While the compensation may vary with region and seniority, the healthcare benefit package, paired with work-life balance and stock options, is why the data analyst role at Google is so desirable. As a result, the interview process is intentionally made competitive to attract and select top-tier talent.
Additionally, Google provides comprehensive training and certification programs to support data analyst career development. The Google Data Analytics Certificate program offers flexible online learning covering essential skills, including data types and structure, and AI-powered productivity tools.
What Is the Interview Process Like for a Data Analyst Role at Google?
The Google Data Analyst Interview is expected to be rigorous and probing into both your technical and behavioral skills. This is how it usually goes:
- Application Submission
- Recruiter / Resume Screen
- Technical Phone Screen
- Virtual Onsite (Full Loop)
- Hiring Committee & Offer
Recruiter / Resume Screen
After you’ve submitted your resume through Google Careers or via referral, it’ll be screened twice. Google uses ATS to initially check the resumes for a surface-level matchup, following it up with a human recruiter review and, in some cases, a hiring manager review for more senior roles. If your resume lacks keywords relevant to the job description, for example, “Python”, “statistical analysis”, and “SQL”, in the case of data analyst roles, it may be filtered out automatically.
The next step in the process is a 30-minute phone or video recruiter screen call. It shouldn’t take you by surprise, as the details of the same will be conveyed to you beforehand. This call is usually focused on assessing your communication skills and cultural fitness—your Googlyness—but…in some cases, our candidates have faced technically probing questions from the recruiter as well, especially if they’re interviewing to join an AI/ML-specific team.
Technical Phone Screen
After passing the recruiter screen, you’ll face the first specialized interview step in the Google data analyst interview process in the form of the technical phone screen. It’s usually taken by a member from the hiring team you’re expected to join, assessing your SQL, statistical, and data storytelling skills. The session usually lasts 30 to 45 minutes and takes place in a shared code editor or sometimes in Google Docs. You’re expected, and encouraged, to think aloud while taking on the problem.
The first 5 to 10 minutes are generally spent on introductions and behavioral questions, where you’ll discuss past projects and your approach to data analytics. This is followed by a live technical question, and the round usually wraps up with a 5-minute Q&A segment.
Virtual Onsite (Full Loop)
After you’ve successfully navigated the nooks and crannies of the previous technical rounds, you’ll be invited to participate in a “Full Loop” to prove your candidacy for the Google Data Analyst Role. This virtual onsite loop usually unfolds in a 4-stage process and stretches to even 6 interviews with hiring managers, team members, cross-functional partners, and senior analysts (45 minutes each) in between. Depending on the number of candidates, interviews for senior L3+ roles often stretch to even the second day.
The first step is the technical deep dive. As the name suggests, it dives deeper into SQL and data manipulation, expecting you to write queries involving multiple joins, window functions, and CTEs. This round may also include a separate interview for the statistics & A/B Testing questions.
The second and third stages are usually combined in a product sense and analytics case (L4+) round, probing into KPIs, mock datasets, and trade-off analysis. This is usually separated into 2 interviews for clarity and better decision-making for the hiring team.
Behavioral and leadership is the next stage, where your Googlyness will be further assessed against Google’s cultural and business values. Your past projects and ethical dilemmas will also be tested during this stage. Be prepared to discuss the tools you’ve used and the challenges you’ve faced in the past.
An optional advanced analytics stage for AI/ML-heavy teams may also appear for some data analyst roles.
Hiring Committee & Offer
Once your virtual onsite interviews conclude, your performance is compiled into what’s known as a candidate packet. This includes detailed feedback from each interviewer, your resume, and any relevant notes or clarifications. Each interviewer is expected to submit feedback within 24 hours, and the packet is then reviewed by an independent hiring committee, not the team you interviewed with, to ensure fairness and consistency in hiring decisions.
One key part of this process is determining your level. For data analyst roles, this is typically between Level 3 (L3) and Level 4 (L4). Candidates recommended for L4 should demonstrate stronger product sense, business impact, and cross-functional leadership. This is why the L4 track often includes an additional product-sense analytics round during the virtual onsite.
What Questions Are Asked in a Google Data Analyst Interview?
The recurring Google data analyst interview questions in this section assess your ability to manipulate data using SQL, identify trends, and build meaningful metrics from raw information.
Coding / SQL & Data-Manipulation Questions
The Google data analyst interview questions in this section assess your ability to manipulate data using SQL, identify trends, and build meaningful metrics from raw information.
1. Write a query to get the number of customers that were upsold
To determine the number of upsold customers, group transactions by user and date, then count distinct dates for each user. If a user has more than one distinct purchase date, they are considered upsold. Use a HAVING clause to filter users with multiple purchase dates and count them.
2. Write a SQL query to find the average number of right swipes for different ranking algorithms
To solve this, join the swipes and variants tables on user_id and filter for the feed_change experiment. Use a RANK function to rank swipes by created_at for each user, then calculate the average right swipes for users who have swiped at least 10, 50, and 100 times by grouping by variant and using a WHERE clause to filter by swipe thresholds.
3. Compute the cumulative sales for each product.
To compute the cumulative sales for each product, perform a self-join on the sales table to match rows with the same product_id and where the date is greater than or equal to the current date. Group the results by product_id and date, and use the SUM() function to calculate the cumulative sum of the price column for each group. Finally, order the results by product_id and date to present the cumulative sales in ascending order.
4. Write a SQL query to count transactions filtered by several criteria
To solve this, use SQL queries to count total transactions, distinct users, transactions with a “paid” status and amount greater than or equal to 100, and identify the product with the highest revenue from “paid” transactions. Use UNION ALL to combine results into a single output table with question IDs and answers.
To calculate the post success rate for each day in January 2020, first count the total posts entered by filtering the events table for post_enter actions. Then, use a LEFT JOIN to count the posts that were successfully submitted (post_submit) on the same day by the same user. Finally, divide the count of successful posts by the total posts entered for each day and group the results by date.
Case/Product Analytics Questions
This part of the Google data analytics interview process focuses on product intuition, experimentation, and decision-making, similar to what’s tested in Google product analyst interview questions and business intelligence analyst Google interview loops:
To measure the success of the audio chat feature, analyze the correlation between successful audio chats and completed transactions. The provided SQL queries join the chats and marketplace_purchases tables to calculate metrics such as the number of connected calls and completed transactions, which can help determine if the feature leads to increased sales.
To explain the scatterplot, analyze the relationship between video length and completion rates, focusing on data clusters, sparsity, and trends. Shorter videos tend to have higher completion rates due to viewer preferences and algorithmic promotion, while longer videos show a decline in completion rates, indicating challenges in retaining viewer attention. The analysis should consider viewer behavior, creator incentives, and TikTok’s algorithmic influences to derive insights for content strategy and platform optimization.
In managing a D2C e-commerce business selling socks, key business health metrics to track include revenue, profitability, inventory levels, and service efficiency. These metrics help in understanding the financial health, ability to meet customer demand, and operational efficiency of the business. Additionally, the timeframe and audience for these metrics, such as board members or day-to-day operations staff, should be considered to tailor the insights appropriately.
The drawbacks of the current data organization include inefficiencies and ambiguities due to sparse datasets with many null values, and column names containing data rather than variable names. To improve analysis, the data should be reformatted to follow tidy data principles, where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Common problems in messy datasets include column headers being values, multiple variables stored in one column, and variables stored in both rows and columns.
10. How to enhance the search feature for activities in San Francisco on Facebook
To improve the search feature, investigate user behavior, search query patterns, and the relevance of search results. Metrics to consider include click-through rates, user engagement, and satisfaction scores, which can help determine if the search functionality is meeting user needs effectively.
Behavioral or Culture-Fit Questions
Google analyst interview questions often explore your collaboration style, how you handle ambiguity, and whether your values align with the company’s data ethics and mission:
11. Describe a data project you worked on. What were some of the challenges you faced?
Google often explores your adaptability and problem-solving approach. Share a specific example where you proactively clarified objectives and iterated on solutions. Discuss how you ensured project progress despite uncertain or changing requirements.
Collaboration and conflict resolution skills are critical to Google. Explain how you navigated differing viewpoints and aligned the team toward a common goal. Emphasize your role in facilitating communication and compromise.
13. How would you answer when an Interviewer asks why you applied to their company?
Google analyst interviews analyze alignment with the company’s culture and ethical standards. Discuss how you prioritize user impact, data privacy, and transparency in your work. Share an example of how you incorporated these values into a project or analysis.
14. Give an example of a time you received critical feedback. How did you respond?
When receiving critical feedback, it’s important to listen actively and understand the perspective of the person providing it. Reflect on the feedback, identify areas for improvement, and take constructive steps to address the issues. Demonstrating a willingness to learn and adapt can turn critical feedback into an opportunity for personal and professional growth.
How to Prepare for a Data Analyst Role at Google
To effectively prepare for a Google Data Analyst role, a structured approach is recommended, with a significant emphasis on technical proficiency. Allocate roughly half of your preparation time to mastering SQL and general coding principles relevant to data manipulation and analysis. This includes practicing complex queries, joins, window functions, and understanding data structures.
Approximately 30% of your focus should be directed towards product sense and metrics-based case studies. This involves dissecting hypothetical scenarios related to Google’s products, defining key performance indicators (KPIs), designing A/B tests, and interpreting analytical outputs to drive business decisions.
The remaining 20% of your preparation should address behavioral competencies, focusing on demonstrating problem-solving skills, collaboration, communication, and alignment with Google’s cultural values through situational examples.
For targeted practice, we highly encourage you to explore our interview questions and Coaching portal. Furthermore, engaging in full mock interview loops is invaluable for simulating the pressure and flow of the multi-round Google interview process. This rigorous and multifaceted preparation strategy, paired with an AI Interview, will help you to confidently navigate the technical and analytical demands of the Google Data Analyst interview.
FAQs
What Is the Average Salary for a Data Analyst at Google?
Average Base Salary
Average Total Compensation
Where Can I Read Real Google Data Analyst Interview Experiences?
You can find first-hand accounts and detailed walkthroughs of the process by exploring online forums and community posts. A helpful place to start is by searching for Google data analyst interview experience threads on our Blog. These often include notes on questions asked, preparation tips, and insights into different team dynamics.
Are There Apprenticeship Paths for Data Analytics at Google?
Yes. Google offers entry-level pathways through its apprenticeship programs, which are ideal for those without traditional four-year degrees or prior full-time experience. If you’re preparing for one of these programs, we recommend reviewing Google data analytics apprenticeship interview questions to get a sense of the behavioral and technical expectations. These interviews tend to focus more on potential and learning agility than advanced technical depth.
Do Business Analysts and Data Analysts Interview Together?
No, they typically go through separate interview loops. While both roles share core areas like SQL and stakeholder communication, the business analyst at Google tends to focus more on product metrics, business case development, and cross-functional insights.
In contrast, the data analyst track often goes deeper into statistics, experimental design, and data architecture. However, some overlap may occur if the hiring team values hybrid skills or if the role blurs between business and data functions.
How Many SQL Questions Should I Expect?
Most candidates face at least one SQL-heavy round in both the technical phone screen and the virtual onsite. On average, you can expect 2 to 3 SQL-focused questions across the process. These will typically involve writing queries using joins, aggregations, subqueries, and sometimes window functions. Reviewing the Google data analyst interview questions list can give you a concrete idea of what types of problems tend to show up and how complex they might get.
Conclusion
Landing a data analyst role at Google is a competitive but achievable goal with the right preparation and mindset. The interview process is thorough, with each stage designed to evaluate your technical proficiency, product intuition, and ability to communicate insights clearly and effectively. From mastering SQL and statistical concepts to navigating behavioral and product-sense interviews, success depends on deliberate, structured preparation.
Start with our Data Analyst Learning Path to build your foundation. Practice with real Data Analyst SQL Interview Questions to sharpen your skills. And if you need a boost of motivation, read Jerry Khong’s success story. All the best!