This article is going to show you what to expect during Google data engineer interviews, as well as what you should do to prepare for them.
We’ve put this guide together using an analysis of Google data engineer interview reports on Glassdoor, candidate feedback on Blind, industry research, and data from other Google engineering positions like software and machine learning engineers.
Here’s the first thing you should know: at Google, technical skills are king, so you’ll need to brush up on your coding, SQL, and data skills.
Let’s get started.
- Interview process and timeline
- Example questions
- SQL interview questions
- Data management interview questions
- Coding interview questions
- Behavioral interview questions
- Preparation tips
What’s the Google data engineer interview process and timeline? It usually takes more than eight weeks and follows the steps below.
1.1 What interviews to expect
- Application, resume, and referrals
- Online assessment (in some cases)
- Phone screens (1-2 interviews, 45-60 min each)
- Onsite interviews (4-5 interviews, 45-60 min each)
Let’s take a look at these steps in more detail.
1.1.1 Application, resume, and referrals
Your first step is to get a Google interview. You can apply directly through their website, or you may have a recruiter reach out to you directly via LinkedIn or email.
At this step in the process, it helps to have a quality and up-to-date resume that is tailored to data engineering positions, and to Google more specifically. Take a look here to get some pointers on crafting a targeted resume for Google.
If you do have a connection to someone at Google, it can be really helpful to get an employee referral to the internal recruiting team, as it may increase your chances of getting into the interview process.
1.1.2 Online assessment (in some cases)
Some candidates will receive an invitation for an online test before moving on to the first-round calls. These are more common for internship and junior positions, but may appear in experienced positions as well.
The test will be assessing your technical skills, with questions similar to the ones you'll be asked in your interviews (e.g. SQL, data structures and algorithms). There may also be a data architecture or pipeline design question as well.
The questions are typically open-ended, requiring you to come up with and defend your own solutions. You’ll likely have a deadline by which you have to complete the assessment, but the test itself is not timed.
1.1.3 Phone screens
The next step includes one or two calls with Google interviewers via Google Hangouts, which last 45-60 minutes each.
Other than ice-breaker questions like “tell me about yourself” or “why Google?”, these phone screens will focus exclusively on your technical skills.
You’ll typically be doing your coding using a shared Google Doc, where you’re unable to run the code or see any static analysis. While most candidates chose to code in Python, you may use a coding language of your choice.
1.1.4 Onsite interviews
Your last hurdle in the Google data engineer interview process is the final onsite or virtual interview loop. This will include four to five separate rounds, which last 45-60 minutes each, and will cover the following topics:
- SQL questions
- Data management questions
- Coding questions (e.g. data structures and algorithms)
- Behavioral questions
We’ll be getting into greater detail on each of these types of questions, including examples, in section 2.
The interviews will be one-on-ones with a mix of people from the team you’re applying to join, including data engineers, software engineers, hiring managers, etc. If you are physically onsite, one of the interviews will take the form of a less formal lunch interview with a future peer.
For your technical interviews (coding, SQL, data), you’ll either be working on a shared Google Doc with your interviewer, or drawing out systems and scripts on a whiteboard. We recommend you practice using these materials at home before going in for the interviews.
1.2 What exactly is Google looking for?
At the end of each interview, including the initial phone screens, each interviewer will grade your performance using a standardized feedback form.
That form is constantly evolving, but we have listed the main components we know of at the time of writing this article below.
A) Questions asked
In the first section of the form, the interviewer fills in the questions they asked you. Future interviewers will take a look at this to make sure that you don't get asked the same questions twice.
B) Attribute scoring
Each interviewer will also assess you on the four main attributes Google looks for when hiring:
- General cognitive ability (GCA). The company wants to hire smart engineers who can learn and adapt to new situations. Here your interviewer will try to understand how you solve hard problems and how you learn. For more information, take a look at our guide to the GCA interview.
- Role-related knowledge and experience (RRK).The company wants to make sure that you have the right experience, domain expertise and competencies for the position you're applying for. For more information, take a look at our guide to the RRK interview.
- Leadership. Google looks for a particular type of leadership called “emergent leadership.” You'll typically be working in cross-functional teams at Google, and different team members are expected to step up and lead at different times in the lifecycle of a project.
- Googleyness. The company wants to make sure Google is the right environment for you. Your interviewer will check whether you naturally exhibit the company's values including: being comfortable with ambiguity, having a bias to action, and having a collaborative nature.
Depending on the exact job you're applying for, these attributes might be broken down further. But the total number of attributes does not usually exceed six or seven.
In this middle section, Google's interviewers typically document your answers to each question in detail, and give you a score for each of the attributes above (e.g. "Poor," "Mixed," "Good," "Excellent").
C) Final recommendation
Finally, interviewers will write a summary of your performance and provide an overall recommendation on whether they think Google should be hiring you or not (e.g. "Strong no hire," "No hire," "Leaning no hire," "Leaning hire," "Hire," "Strong hire").
1.3 What happens behind the scenes
If things go well at your onsite interviews, here is what the final steps of the process look like:
1. Interviewers submit feedback
After your onsite, your interviewers will all submit their feedback usually within two to three days. This feedback is then passed on to the hiring committee.
2. Hiring committee recommendation
The hiring committee will review the interviewers’ feedback, along with your resume, internal referrals, and any past work you have submitted. At this stage, the hiring committee will make a recommendation on whether Google should hire you or not.
3. Team matching
If the hiring committee recommends that you get hired, you'll usually start your team matching process. In other words, you'll talk to hiring managers and one or several of them will need to be willing to take you in their team in order for you to get an offer from the company.
4. Senior leader and Compensation committee review
In parallel, the hiring committee recommendation will be reviewed and validated by a Senior manager, and a compensation committee who will decide how much money you are offered.
5. Final executive review (only senior roles)
If you are interviewing for a senior role, a Senior Google executive will review a summary of your candidacy and compensation before the offer is sent to you.
6. You get an offer!
As you've probably gathered by now, Google goes to great lengths to avoid hiring the wrong candidates. This hiring process with multiple levels of validation helps them scale their teams while maintaining a high caliber of employees. But it also means that the typical process can spread over multiple months.
Let’s get into the four primary categories of questions you’ll face in the Google data engineer interview.
We’ve analyzed every interview question reported by Google data engineer candidates on Glassdoor, collected dozens of interview reports on Blind, and consulted data engineer interview data from Google and other top companies.
According to our research, here are the most commonly asked questions in Google data engineer interviews:
In the sections below, we've put together a high-level overview of each type of question, to help you prepare.
Additionally, we've compiled a selection of real data engineer interview questions, according to data from Glassdoor and Blind. Note that we’ve edited the language in some places to improve the clarity or grammar.
With Google’s backend handling billions of data fetch operations daily, engineers must be able to extract and organize huge volumes of structured and unstructured data. This requires extensive experience with SQL.
You’ll need to know how to use select statements and where clauses, joins, aggregations, and in many cases window functions and lag/lead. Be prepared to explain why you choose certain approaches over other options, and why yours is the most optimal.
Let’s take a look at some real examples of SQL questions that we found in data engineer Glassdoor interview reports. The first question is from Google interview reports, and the rest are a combination of Facebook and Amazon SQL questions, to round out your preparation.
Data engineer interview questions: SQL
- Given X scenario, create tables with Y parameters, and write the appropriate queries.
- Given a dataset, find the time period when the most people were online, measured in seconds.
- Given a large table with 3 columns (datetime, employee, and customer_response, which is a free text column), with phone number information embedded in the customer_response column, find the top 10 employees with the most phone numbers found in the customer_response column.
- A table has two data entries every day for # of apples and oranges sold. Write a query to get the difference between the apples and oranges sold on a given day.
- Given a database schema showing product sales: calculate what percent of our sales transactions had a valid promotion applied? And what % of sales happened on the first and last day of the promotion?
- Given full authority to "make it work," import a large data set with duplicates into a warehouse while meeting the requirements of a business intelligence designer for query speed.
Google must collect huge amounts of data in order to remain at peak performance. Modeling, warehousing, and moving that data from one spot to another is key to keeping its systems up and running.
So interviewers will be testing you on your ability to bring datasets together to solve realistic problems that Google data engineers face daily. This may require designing data marts to support analytics use cases, designing etl pipelines, implementing Big Data solutions, and using SQL to produce specific results.
Let’s take a look at some real examples of data management questions that we found in the Glassdoor data. The first three questions are from Google DE interview reports, and the rest are from Amazon and Facebook.
Data engineer interview questions: Data management
- What type of technology would you need to build YouTube?
- How would you design a video streaming service architecture?
- Build and design your own tree.
- How do you create a schema that would keep track of a customer address where the address changes?
- Design a data model in order to track product from the vendor to the Amazon warehouse to delivery to the customer.
- Give a schema for a data warehouse.
- Can you design a simple OLTP architecture that will convince the Redbus team to give X project to you?
- Given a schema, create a script from scratch for an ETL to provide certain data, writing a function for each step of the process.
- How would you build a data pipeline around an AWS product, which is able to handle increasing data volume?
Data engineers at Google solve some of the company’s biggest problems through code. So interviewers will be testing your problem-solving skills and ability to think in a structured way using code.
Interviewers may ask you to manipulate data structures, use dictionaries, implement loops and lists, and show a good understanding of string, set operations, recursion, etc. All of this may be done in your coding language of choice.
We recommend reading our guide on how to answer coding interview questions to understand more about the step-by-step approach you should use to solve these questions.
The first two questions below come from Google data engineer interview reports, and the rest are coding questions from Google software engineer interview reports.
Data engineer interview questions: Coding
- How would you get top ten data (from last column) from a comma separated flat file?
- How would you sum all values in a range of values between A and B?
- Given a binary tree, find the maximum path sum. The path may start and end at any node in the tree. (Solution)
- Given an encoded string, return its decoded string. (Solution)
- Implement a SnapshotArray that supports pre-defined interfaces (note: see link for more details). (Solution)
- Given a string S and a string T, find the minimum window in S which will contain all the characters in T in complexity O(n). (Solution)
- Given a matrix and a target, return the number of non-empty submatrices that sum to target. (Solution)
Beyond possessing outstanding technical skills, Google data engineers must have the soft skills necessary to work in cross-functional teams. Data engineers with management positions must also prove that they have what it takes to lead teams.
Interviewers test these capabilities by asking behavioral questions, which assess you on your past behavior, looking to see if you have demonstrated good leadership, communication, and teamwork skills. To dig deeper into behavioral questions, check out our guide on how to answer them, as well as our take on the "Why Google?" question.
The first three questions below come from Google data engineer interview reports, and the rest are behavioral questions from Google software engineer interview reports.
Data engineer interview questions: Behavioral
- Why Google?
- Tell me about your past experience with agile work.
- If a customer says X to you, how would you respond?
- Tell me about a recent / interesting project you worked on.
- Tell me about a time you had to resolve a conflict in a team.
- What is your favorite Google product?
- Tell me about a time you had to handle trade offs and ambiguity.
- Tell me about a time you were part of an organization in transition and how you helped them move forward.
- Tell me about a time you used data to make a critical decision.
- Tell me about a time you used data to measure impact.
Now that you know what questions to expect, let's dive into how to prepare. Below, we've listed the four steps we recommend taking to prepare as efficiently as possible.
3.1 Learn about Google’s culture
This is a key step that many candidates fail to take. Before investing your time in preparing for an intense and lengthy interview process like Google’s, you should make sure that it is the right company for you.
Because Google is a well-known and powerful company, many assume that they should apply there, without considering the position more carefully. However, prestige alone does not mean that the job is a good fit for everyone.
If you know engineers who work at or used to work at Google, reach out to them to find out more about the culture and daily tasks. We also recommend you do your research, starting with the following resources:
- Google's mission statement (by Google)
- Google's values (by Google)
- Google strategy teardown (by CBS Insights)
3.2 Practice by yourself
Once you’ve learned more about Google, study up on the types of questions that you’ll be asked during the interviews
For behavioral questions, we recommend learning our step-by-step method for answering this type of question.
For data questions, learndatamodeling.com has some useful videos that explain some of the core concepts of data modeling, and this article goes over helpful best practices when it comes to data warehousing.
Once you’re ready to answer the four types of interview questions above, start by interviewing yourself out loud. It will likely feel strange at first, but it will significantly improve the way you communicate your answers.
Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. Trust us, it really helps!
3.3 Practice with peers
Practicing by yourself will only take you so far. A huge challenge of technical interviews is that you have to communicate what you are doing as you are doing it.
Even candidates who have excellent technical skills can fall short in interviews because they are used to working alone, in silence. Thinking out loud for the benefit of the interviewer is a learned skill, key to landing an offer at top tech companies.
To get better at this, we strongly recommend doing mock interviews. You can start by practicing with a peer or family member interviewing you.
3.4 Practice with ex-interviewers
Practicing with peers is a great starting point for mock interviews, as it’s free and easy to set up. However, unless your peers have experience interviewing candidates at Google, they will not be able to give you the targeted feedback necessary to land the role.
Once you reach that stage, we recommend practicing with ex-interviewers from top tech companies.
Of course, not everybody is fortunate enough to know a Google ex-interviewer at all, much less one who has the time to schedule multiple mock interview sessions.
That’s where we come in. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today.