Top 23 Google Data Engineer Interview Questions + Guide in 2024

As Google shifts its focus toward artificial intelligence and enhanced cloud services in 2024, the role of its data engineers will become increasingly significant. As an engineer, you’ll be tasked with finding creative ways to leverage the vast amounts of data Google collects. Your responsibilities could span various aspects of data handling, from infrastructure design and machine learning to data quality assurance and security.

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

In this interview guide, we’ll walk you through sample Google data engineer interview questions as well as tackle the interview process, strategies, and provide a few 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.

This role will test your expertise in data modeling, coding, and, most importantly, problem-solving. Further, they want engineers who communicate well, exhibit leadership, and fit into Google’s culture.

Please note that the questions and structure of the interview process will differ based on the team and function advertised in the job ad. Always read through the job description carefully while 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.

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: Technical Screening(s)

The next step includes one or two 45-minute technical rounds with Google interviewers. You’ll be asked to code on a shared Google Doc. One interesting feature of Google’s coding rounds is that interviewers aren’t concerned about the language—they simply want to assess your ability to program. Although most candidates code in Python, you can choose the language you are most comfortable with.

Step 3: Onsite Interviews

If you’ve made it this far, you will be invited onsite to meet your team and have up to 5 rounds of interviews, each an hour long. These typically involve technical, behavioral, and case study questions.

Google’s data engineering interview questions primarily focus on practical skills in data manipulation, query optimization, algorithm design, and problem-solving in real-world data engineering scenarios. The questions test your cognitive ability, role-related knowledge, leadership, and overall “Googliness.” The hiring team at Google defines Googliness 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.

For a more in-depth look at these questions, let’s go through the list we have below:

1. Describe a time you used your values to ensure a diverse team where everyone was included.

Google espouses diversity and inclusion, and they will want to know if you can foster such an environment.

How to Answer

Addressing why you think diverse perspectives should be respected is a good idea.

Example

“I led a project team with members from various cultural backgrounds. I initiated team-building exercises and encouraged open dialogue about any tension or conflict in the team. Making sure everyone was heard also contributed significantly to tailoring our product’s user interface for global markets, directly impacting its success in several regions.”

2. Why do you want to join Google?

This question 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 opportunities that attract you to the company. Be honest and specific about how Google’s offerings align with your career goals.

Example

“I am deeply 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 that have 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,” as the ability to learn from mistakes is essential for Googlers to stay on the cutting edge of innovation.

How to Answer

Select a time when you faced a setback at work 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 was tasked with optimizing 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 communicating with the team and stakeholders, informing them of progress and setbacks.”

4. How do you resolve conflict with co-workers or external stakeholders?

The interviewer needs to understand how you handle conflicts in a team setting, as data engineering at Google often requires close collaboration with various teams.

How to Answer

Illustrate with a concise example that highlights your initiative and emotional intelligence.

Example

“In a past project, I worked with a team member who tended to make unilateral decisions and had difficulty effectively communicating their thought process.

Realizing this was affecting our productivity and team dynamics, I requested a private meeting with this colleague. I tried to understand their perspective while expressing the team’s concerns constructively. During our conversation, I learned that their approach stemmed from a deep sense of responsibility and a fear of project failure. I acknowledged their commitment and then elaborated on how collaborative decision-making could enhance project outcomes.

We agreed on a more collaborative approach, with regular briefings that clearly outlined updates. This experience taught me the value of addressing interpersonal challenges head-on but with empathy. The situation improved significantly after our discussion.”

5. Can you 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 effort, 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. So, 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 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.”

7. Given n dice, each with m faces, write a function to list all possible combinations of dice rolls. Can you also do it recursively?

Google engineers frequently handle tasks involving simulations or probabilistic calculations, especially when creating scalable data processing solutions. This question checks how you translate complex logical problems into implementable code.

How to Answer

Define the function and briefly describe your approach, for example, to utilize built-in functions to efficiently generate all individual face value combinations and yield each as a tuple. It’s also beneficial to mention the trade-offs between iterative and recursive solutions in terms of readability and performance.

Example

“For massive datasets, efficiency should be prioritized with an iterative approach like the product function. It will generate all individual face value combinations and yields them as tuples, minimizing runtime and maximizing performance.”

8. You are given two non-empty linked lists representing two non-negative integers. Each list contains a single number, where each item in the list is one digit. The digits are stored in reverse order. Can you add the two numbers and return the sum as a linked list, also with the digits in reverse order?

This problem tests algorithmic thinking and understanding of data structures, essential for developing scalable systems.

How to Answer

Describe the process of iterating through both lists simultaneously, adding corresponding digits, and handling carry-over values.

Example

“I’d iterate through each list node-by-node, summing the digits plus any carry from the previous digit’s sum. If the sum is over 9, I would store the remainder and carry over 1 to the next sum. This simulates how you’d add numbers on paper but in reverse. This would continue until both lists are fully traversed, handling any leftover carry.”

9. How would you implement a binary search algorithm?

Understanding basic concepts like binary search is imperative. A data engineer at Google will often need to use this algorithm to retrieve data quickly from sorted datasets.

How to Answer

Describe the binary search algorithm, emphasizing its efficiency and the conditions under which it operates (e.g., the data must be sorted). Explain the step-by-step process of dividing the search interval in half and how the search space is reduced at each step.

Example

“I would start by identifying the low and high boundaries of the array (or list) containing the data. The algorithm then enters a loop where it calculates the midpoint of the low and high boundaries. If the element at the midpoint is equal to the target value, the search is successful, and the index is returned. If the target value is less than the element at the midpoint, the algorithm repeats for the lower half of the array (adjusting the high boundary). If the target value is greater, it repeats for the upper half (adjusting the low boundary). This process continues until the element is found or the low boundary exceeds the high boundary, indicating the element is not in the array. Binary search is efficient with a time complexity of O(log n), making it suitable for large datasets.”

10. Given a table of bank transactions with columns id, transaction_value, and created_at representing the date and time for each transaction, write a query to get the last transaction for each day.

In a real-world scenario, you might need to extract similar insights from transactional data for daily financial summaries or end-of-day reports.

How to Answer

Focus on using a window function to partition the data. Explain the function and how the ORDER BY clause helps determine the latest transaction.

Example

“To write this query, I would use a window function like ROW_NUMBER(), partitioning the data by the date portion of the created_at column and ordering by created_at in descending order within each partition. This setup will assign a row number of 1 to the last transaction of each day. Then, I would wrap this query in a subquery or use a CTE to filter out the rows where the row number is 1. The final output would be ordered by the created_at datetime to display the transactions chronologically. This approach ensures we get the last transaction for each day without missing any days.”

11. Write a Python script that fetches data from an API and updates a database. Include comprehensive error handling to manage rate limiting, timeouts, and intermittent API unavailability.

You’ll need to write robust code that can handle errors well, which is crucial for maintaining system integrity.

How to Answer

You should mention and elaborate on the Python library you choose. It should support the following: making HTTP requests, error handling using try-except blocks, and strategies for dealing with common API issues like rate limits and timeouts.

Example

“I’d use the requests library to handle API calls, wrapping each call in a try-except block to catch exceptions related to connectivity issues, timeouts, and rate limits. For handling rate limits, the script would detect a 429 status code and use a backoff strategy, retrying the request after a delay. To address intermittent API unavailability, I’d implement a retry mechanism with exponential backoff and jitter, ensuring the script attempts to fetch the data several times before failing.”

12. Find and return all the prime numbers in an array of integers. If there are no prime numbers, return an empty array.

This question assesses your ability to write basic functions and understand optimization techniques.

How to Answer

Discuss an efficient method for identifying prime numbers within an array, such as by checking divisibility only up to the square root of each number and skipping even numbers after 2.

Example

“The solution should involve iterating through the array and checking each number to see if it’s prime. I’d do this by verifying that it’s only divisible by 1 and itself, optimizing by checking divisibility up to its square root. If no divisors are found, it is prime and added to a result list. This method is similar to a sieve algorithm and would be efficient for tasks like filtering user input data.”

13. Discuss the design schemas you would use to enhance scalability in Google Cloud Platform services.

This question assesses your understanding of cloud architecture and ability to apply scalable design principles.

How to Answer

It is essential to ask clarifying questions and understand the scope of the business problem before explaining the solution. While answering, discuss using specific GCP services and architectural patterns that enhance scalability, such as microservices and serverless architectures, and the use of managed services like Bigtable and Spanner.

Example

“I would ask about the expected user load, data volume growth over time, and specific regions we’re targeting. This would help determine whether to opt for a regional or global approach in our cloud infrastructure.

Based on the business needs, I’d likely start with a microservices architecture to ensure each component can scale independently. For data storage, if we’re dealing with high-volume, non-relational data, I’d consider Bigtable for its ability to scale horizontally. For global applications requiring strong consistency and horizontal scalability, Spanner would be my go-to option, especially for transactional data. Additionally, implementing Cloud Functions for event-driven, serverless computing would allow us to pay only for what we use.”

14. Design a database for a stand-alone fast food restaurant. Based on the above database schema, write an SQL query to find the top three highest revenue-generating items sold the previous day.

With this problem, the interviewer would assess your ability to structure data efficiently for real-world applications like inventory management or order processing.

How to Answer

Focus on a relational model that covers all aspects of restaurant operations. Explain your choice of tables and relationships, and emphasize the importance of query efficiency.

Example

“I’d start with core tables like MenuItems, Orders, OrderDetails, and Employees. MenuItems would store menu options, prices, and categories. Orders would track each transaction, linked to Customers for CRM potentials and Employees for performance metrics. OrderDetails connects orders to specific menu items, including quantities. This setup would support operations, facilitate detailed reporting, and optimize for queries related to sales trends and inventory management, similar to managing product data in a Google Cloud environment.”

15. For a SaaS product generating vast amounts of event data, describe how you would design a scalable data warehousing solution.

This assesses your understanding of warehousing principles and the integration of diverse data sources into a cohesive, queryable system.

How to Answer

Mention incorporating cloud-based solutions, data partitioning, and indexing to improve query performance.

Example

“I would start with a cloud-based data warehouse like Google BigQuery that offers scalability and managed services. Data would be ingested using a reliable data pipeline framework such as Apache Kafka, ensuring that data from various sources is consolidated efficiently. Lastly, the warehouse would be organized into subject-oriented schemas to facilitate analytics.”

16. Let’s say you’re tasked with building the YouTube video recommendation algorithm. How would you design the recommendation system?

Understanding machine learning algorithms and applying them to large-scale problems are essential challenges that Google engineers tackle in their projects.

How to Answer

Outline a strategy incorporating user data, video metadata, and engagement metrics. Suggest the machine learning models you would use to build the most robust recommender system.

Example

“I’d particularly focus on a hybrid model combining collaborative filtering for leveraging user interaction patterns and content-based filtering to match video metadata with user preferences. This approach ensures personalized recommendations by considering what similar users liked and the content’s attributes. Iterative testing and refining using TensorFlow on Google Cloud would allow for optimizing the model’s accuracy and user satisfaction.”

17. What are the main components of a MapReduce job?

Understanding distributed computing concepts is essential for efficiently processing large datasets, such as Google’s search indexing.

How to Answer

Briefly describe each component’s role—Mapper, Reducer, and job configuration—highlighting their contribution to the data processing workflow.

Example

“A MapReduce job has three main components: the Mapper, which processes and filters input data into key-value pairs; the Reducer, which aggregates these pairs based on the keys; and the job configuration, which sets up the task’s parameters, including input/output paths and the classes handling map and reduce functions. This framework allows for efficient, scalable data processing across multiple nodes.”

18. When are support vector machines preferable to deep learning models?

This question gauges your concept of algorithms’ strengths in specific contexts, such as when data is limited or highly dimensional.

How to Answer

Emphasize situations where SVMs outperform deep learning models due to their efficiency in high-dimensional spaces, smaller datasets, and when the margin of separation is crucial.

Example

“SVMs are preferable when we have limited data and high dimensionality and where finding a hyperplane for classification is efficient. For example, in text classification, where datasets might not be big enough to train deep learning models without overfitting. SVMs, with their kernel trick, can handle high-dimensional data and provide robust models in such instances.”

19. What is the Hadoop Distributed File System (HDFS)? How does it differ from a traditional file system?

This question checks your understanding of big data storage solutions, which are imperative for handling large-scale processing tasks at Google.

How to Answer

Your answer should focus on aspects like scalability, fault tolerance, data distribution, and how HDFS manages large datasets.

Example

“Unlike traditional file systems, HDFS spreads data across many nodes, allowing it to handle petabytes of data. HDFS is highly fault-tolerant; it stores multiple copies of data (replicas) on different machines, ensuring that data is not lost if a node fails. It is designed to work with commodity hardware, making it cost-effective for handling massive amounts of data. HDFS is tightly integrated with the MapReduce programming model, allowing for efficient processing.”

20. Let’s say you’re setting up the analytics tracking for a web app. How would you create a schema to represent client click data on the web?

Understanding client click data is essential for enhancing user experience. The interviewer is evaluating your ability to determine what data is important to capture, how to organize it for efficient analysis, and your approach to data modeling.

How to Answer

Follow this approach to answer such questions: 1) Ask clarifying questions; 2) Assess requirements; 3) Present your solution; 4) Create a validation plan to assess and iterate the solution for continuous improvement.

Example

“In creating such a schema, I would capture essential attributes such as the user ID (to uniquely identify users), session ID (to track individual user sessions), timestamp (to record the exact time of the click), page URL (to identify which page was clicked), and click details (like the element clicked and the type of click). Capturing metadata such as the user’s device type, browser, and geographic location can also provide valuable insights.

The schema should be designed for efficient querying, so I would normalize the data. For high query performance and scalability, especially with large data, a NoSQL database like MongoDB might be more suitable than a traditional SQL database. This allows for more flexibility in handling semi-structured data and can easily scale with the growth of the web app’s user base.

In terms of data storage, I would consider using a time-series database or a columnar storage format if the primary analysis involves time-based aggregations or rapid querying of specific columns.”

21. How would you ensure the data quality across different ETL platforms?

In this question, you have been tasked to look at the ETL pipeline connecting data marts with the survey platform’s data warehouses. Within this layer also comes another layer of ETL pipelines, connecting transactional data stores with the survey platform’s data warehouse, as well as the pipeline normalizing this data through translation modules.

How to answer

Before we can ensure data quality, we first need to define our standards for data quality. In this case, we might focus on determining the data’s accuracy, consistency, and timeliness.

Example

“In the context of analytics and market research, timeliness with a latency of ≥ 24 hours is not a major concern, allowing us to focus on other aspects. To ensure accuracy, we should test translation modules through back-translation and compare semantic scores to preserve the true intent and sentiment. Proper data transformations are crucial, particularly in multinational surveys where timezone differences can impact real-time analytics. Standardized timestamps or a reference time zone can mitigate these issues. Ensuring data completeness and viability is also important, necessitating collaboration with analytics teams to meet their requirements for an effective analytics pipeline.”

22. Let’s say we want to launch a re-design of a landing page to improve the click-through rate. Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?

This question checks your understanding of A/B testing and the statistical methods used to determine the significance of the results, which are crucial for data-driven decision-making in a redesign project.

How to Answer

Your answer should focus on aspects like hypothesis testing, confidence intervals, p-values, and the importance of sample size in determining statistical significance.

Example

“To infer whether the results of the click-through rate (CTR) are statistically significant, we would start by defining a null hypothesis, typically stating that there is no difference in CTR between the control and variant groups. We would then calculate the p-value, which indicates the probability of observing the results under the null hypothesis. If the p-value is below a pre-defined threshold (commonly 0.05), we reject the null hypothesis, suggesting that the change in CTR is statistically significant. Additionally, it’s important to ensure that the sample size is large enough to detect a meaningful difference and to compute confidence intervals to understand the range within which the true effect lies.”

23. Let’s say that your company is running a standard control and variant AB 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 ?

This question checks your understanding of assessing the validity of A/B test results, which is crucial for making data-driven decisions about product features.

How to Answer

Evaluating the statistical significance of test results and ensuring the proper setup of the A/B test are key steps in determining the validity of the outcome.

Example

“To assess the validity of the .04 p-value, I would first examine the setup of the A/B test, ensuring that the control and variant groups were appropriately randomized and that external factors were controlled. For instance, it’s important to verify that the distribution of users across different traffic sources is consistent between groups. Next, I would consider the sample size and the duration of the test. A small sample size or prematurely ending the test could lead to misleading p-values. Moreover, I would check if the p-value was measured at multiple intervals, as repeated monitoring can inflate the likelihood of finding a statistically significant result by chance. To avoid such errors, it’s best to pre-determine the minimum effect size and calculate the necessary sample size and duration before starting the experiment.”

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

Study the Company and Role

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

Then, once you understand more about the company, seek to learn how the team you are applying to supports its goals.

Visit Google’s careers page for more details about its hiring process. To learn more about what Google wants in an ideal employee, read this article on Googliness.

Brush Up on Technical Skills

Make sure you have a strong foundation in programming, SQL, and data structures. Familiarity with cloud computing platforms like GCP is also a plus. However, keep in mind that the main objective of the interview is to assess your fundamental concepts and first principles thinking. So, your goal should be to study data structures, tools, algorithms, etc., in the context of real-world problems.

Check out the resources we’ve tailored for data engineers: a case study guide, a compendium of data engineer interview questions, data engineering projects to add to your resume, and a list of great books to help you on your engineering journey. If you need further guidance, consider our tailored data engineering learning path.

Prepare Behavioral Interview Answers

Soft skills such as collaboration, effective communication, and problem-solving are paramount to succeeding in any job, especially in a collaborative culture like Google’s.

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

Keep Up with the Industry

The data engineering landscape is constantly evolving, so keep yourself updated on the latest technologies, news, and best practices.

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.

Check out our complete Data Engineer Prep Guide to ensure you don’t miss anything important while preparing for your interview at Google.

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

The average base salary for a data engineer at Google is $148,185, considerably higher than the average salary for the average data engineering role in the US.

For more insights into the salary range of a data engineer at various companies, segmented by city, seniority, and company, check out our comprehensive Data Engineer Salary Guide.

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

Here is our discussion board, where our members talk about their Google interview experience. You can also use the search bar to look up data engineer interview experiences with other firms to gain more insight into interview patterns.

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

We have Google data engineer jobs listed, which you can apply for directly through our job portal. You can also look at similar roles relevant to your career goals and skill set.

Having success in answering Google data engineer interview questions requires a strong foundation in technical skills and problem-solving, as well as the ability to demonstrate leadership, collaboration, and Googliness.

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 business analyst, data analyst, scientist, and other roles in our main Google interview guide.

For more information about interview questions for data engineers, peruse our main data engineering interview guide and case studies, as well as our Python and SQL sections.

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!