Google Data Engineer Interview (process, questions, prep)

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, for instance.

Let’s get started.

• Role and salary
• Interview process and timeline
• Example questions
  • SQL interview questions
  • Data management interview questions
  • Coding interview questions
  • Behavioral interview questions
• Preparation tips

Click here to practice 1-on-1 with a data engineer ex-interviewer

1. Google Data Engineer Role and Salary ↑

Before we cover your Google data engineer interviews, let’s first look at the role itself.

Google data engineer roles are diverse, covering many different specific functions and areas, such as:

• Data Engineer, YouTube Marketing
• Physical Security Data Engineer
• Staff Software Engineer, YouTube Trust and Safety Data
• Strategic Cloud Engineer, Data and Analytics
• Data Center Management Network Engineer

1.1 What does a Google data engineer do?

As a Google data engineer, you will process and transform raw data to a usable format, which is then further analyzed by other teams. You will also cleanse, organize, and manipulate data using pipelines

You will also have to collaborate with engineers, marketing teams, product managers, sales associates, etc. These collaborations will help you to identify customer behavior and enhance network structure optimization accordingly.

Google’s Cloud Platform spans across many different verticals and has hundreds of services across App Dev, Data, Infra, ML, and Security. This means that they need a large number of technically experienced data engineers and other professionals for each vertical.

If you want to work as a Google data engineer, you need to have essential technical and soft skills.

Technical skills include good facility with programming languages such as Python, Scala, and C++, as well as deep knowledge of SQL, data structures and algorithms, data warehousing, data modelling, ETLs, Kafka and using Kafka with Hadoop, UNIX and GNY/Linux systems.

Soft skills emphasize good communication and collaboration skills, as well innovativeness, adaptability, flexibility, and sound decision-making.

1.2 How much do Google data engineers make?

Based on Levelsfyi data,  data engineers’ total compensation at Google in the United States ranges from $169.2K per year for L3 to $358K per year for L6. The median compensation in a United States package is $268K. Google’s compensation packages typically involve base salary, stock, and bonuses. 

Here is the breakdown of Google’s compensation packages for different data engineer levels.

If you’re unsure what level you’re being considered for, ask your recruiter. Location and years of experience will also influence differences in salary. 

Ultimately, how you do in your interviews will help determine what you’ll be offered. That’s why hiring one of our ex-Google interview coaches can provide such a significant return on investment.

And remember, compensation packages are always negotiable, even at Google. So if you do get an offer, don’t be afraid to ask for more. Use this Google offer negotiation guide to help you. If you want to receive practical advice and ask more questions on how to actually negotiate, consider booking one of our salary negotiation coaches to get expert advice.

2. Interview process and timeline ↑

What’s the Google data engineer interview process and timeline? It usually takes more than eight weeks and follows the steps below.

2.1 What interviews to expect

1. Resume screen
2. Online assessment (in some cases)
3. Phone screens (1-2 interviews, 45-60 min each)
4. Onsite interviews (4-5 interviews, 45-60 min each)

Let’s take a look at these steps in more detail.

2.1.1 Resume screen

First, recruiters will look at your resume and assess if your experience matches the open position. This is the most competitive step in the process, as millions of candidates do not make it past this stage.

If you’re looking for expert feedback on your resume, you can get input from our team of ex-FAANG recruiters, who will cover what achievements to focus on (or ignore), how to fine tune your bullet points, and more.

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. According to Glassdoor data, 8% of data engineer candidates who got interviews with Google were through employee referrals.

2.1.2 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. 

2.1.3 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.

2.1.4 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 3.

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.

2.2 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:

1. 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.
2. 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.
3. 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. For more information, check out our Googleyness and leadership interview guide.
4. 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. For more information, read our Googleyness and leadership interview guide.

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").

2.3 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.

3. Example questions ↑

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:

• SQL questions
• Data management questions
• Coding questions
• Behavioral questions

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.

3.1 SQL interview questions ↑

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.

3.2 Data management questions ↑

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, within certain data environments.

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 data engineer interview reports, and the rest are from Amazon and Facebook.

3.3 Coding interview questions ↑

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. 

Finally, we recommend reading this guide on how to answer coding interview questions and practicing with this list of coding interview examples in addition to those listed below.

The first two questions below come from Google data engineer interview reports, and the rest are coding questions from Google software engineer interview reports.

3.4 Behavioral interview questions ↑

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.

4. Preparation tips ↑

Now that you know what questions to expect, let's focus on preparation. 

Below, you’ll find links to free resources and four introductory steps to help you prepare for your Google data engineer interviews.

4.1 Deep dive into the product / organization↑

As you've probably figured out from the example questions listed above, it's important to be familiar with Google's products and its organization. You'll therefore need to do some homework before your interviews.

Here are some resources to help you get started:

• Alphabet annual reports and strategy presentations (by Alphabet)
• Google strategy teardown (by CB Insights)
• Google org culture analysis (by Panmore Institute)
• Life at Google (Inside Google blog)

4.2 Learn a consistent method for answering data engineer interview questions ↑

As mentioned previously, Google will ask you questions that fall into these categories: behavioral, SQL, coding, and data management and design questions.

Approaching each question with a predefined method will enable you to build strong interview habits. Then, when it comes time for your interviews, these habits will reduce your stress and help you to make a great impression.

Learn how to methodically answer these questions (and more) from these resources:

• Behavioral: Google behavioral interview questions
• SQL: The 50 Best Resources for Learning SQL
• Coding: Google Python Style Guide and coding challenges at HackerRank and LeetCode
• Data management / design: Databases - system design interview concepts
• Designing ETL pipelines: Designing an effective ETL pipeline 
• Batch processing and streaming: Apache Spark for batch processing, Apache Kafka for data streaming
• Environment: Google Cloud Platform (GCP), Docker, scripting, Terraform, Kubernetes

Once you’re in command of the subject matter, you’ll want to practice answering questions. But by yourself, you can’t simulate thinking on your feet or the pressure of performing in front of a stranger. Plus, there are no unexpected follow-up questions and no feedback.

That’s why many candidates try to practice with friends or peers.

4.3 Practice with peers

If you have friends or peers who can do mock interviews with you, that's an option worth trying. It’s free, but be warned, you may come up against the following problems:

• It’s hard to know if the feedback you get is accurate
• They’re unlikely to have insider knowledge of interviews at your target company
• On peer platforms, people often waste your time by not showing up

For those reasons, many candidates skip peer mock interviews and go straight to mock interviews with an expert. 

4.4 Practice with experienced interviewers

In our experience, practicing real interviews with experts who can give you company-specific feedback makes a huge difference.

Find a Google data engineer interview coach so you can:

• Test yourself under real interview conditions
• Get accurate feedback from a real expert
• Build your confidence
• Get company-specific insights
• Learn how to tell the right stories, better.
• Save time by focusing your preparation

Landing a job at a big tech company often results in a $50,000 per year or more increase in total compensation. In our experience, three or four coaching sessions worth ~$500 make a significant difference in your ability to land the job. That’s an ROI of 100x!

Click here to book data engineer mock interviews with experienced interviewers