Google Data Engineer Interview Guide

The gist

What’s unique about the data engineer interview at Google:

• Conversational and encouraging interviewers
• Questions with preplanned layers
• Trick questions and bespoke questions

The data engineering process is quite similar to Google's software engineering interview loop, with fewer coding rounds in exchange for SQL and data management rounds.

The coding questions aren’t as hard for data engineers as they are for software engineers. If you’re a data engineer, prep for easy/medium coding problems. The SQL bar is higher; expect medium/hard questions. Since they provide cloud services like Google Cloud Platform, Google’s data engineer interview process focuses more on advanced cloud tool usage.

At Google, the data engineering levels and average total compensation:

• L3 (junior): $169k
• L4 (mid-level): $236k
• L5 (senior): $282k
• L6 (staff): $350k

What does a Google Data Engineer do?

Google Data Engineers will commonly handle the typical tasks of a data engineer (data pipelines, data warehousing, ETL). Other responsibilities they can have may include more general software engineering, a greater focus on cloud computing, and sometimes client-side web technologies for certain roles.

Before you apply

1. Search the most popular data engineering questions being asked at top tech companies.
2. Prepare for difficult SQL and data modeling rounds. These are the most difficult portions of the Google data engineering process.
3. Sharpen your skills in cloud-related technologies. Google is invested in the cloud and expects its data engineers to be the same. Study the Hadoop ecosystem, and the Google Cloud Platform ecosystem (including BigQuery, and DataFlow).

Interview process

Google has a team-independent process, so all candidates go through a similar interview process, which is:

• A recruiter screen, which is which is typical when compared to similar companies in the industry
• One or two technical screens focused on SQL and coding
• The final round, consisting of 4–6 rounds (mainly technical interviews)

Recruiter

These rounds are fairly easy; Google has less aggressive recruiters than Meta or Amazon. Overall, it’s a typical big tech recruiter round: expect to be asked about your job search, motivations, past work, and compensation expectations.

Sample questions include:

Why do you want to work at Google?

Asked at Google 

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• Walk me through your resume.

Technical

Candidates usually do one tech screen in this loop, but it’s possible to get two. Each will last 45–60 minutes. The topics covered will be SQL, coding, data architecture, or pipeline design.

One common structure for these interviews is to build a data model based on an ambiguous prompt and then write complex SQL queries using the data model you built.

Topics to expect:

• Data structures and algorithms
• Python questions
• SQL
• Data-based questions
  • Big data
  • Tools in the Hadoop ecosystem (like Spark and Hive)
  • Big data processing pipelines
  • Batch pipelines
  • Stream pipelines
  • Cloud technologies

Sample questions include:

• Design a relational database for a specific business case.
• Given a table with measurement values from a Google sensor with measurements taken across days, multiple times each day. Calculate the sum of odd-numbered and even-numbered measurements separately for a particular day and display the results in two different columns.
• Write a query to obtain number of monthly active users in October 2024, including the month in numerical format "1, 2, 3".

Final round

Expect 4–6 rounds lasting 45 minutes to 1 hour, primarily technical, with one behavioral round.

Interview questions

Behavioral

Google has the easiest behavioral round in FAANG partly because their interviewers are limited to pre-approved questions. Also, because behavioral rounds at Google have faced some internal rebellion from engineers, with the popular stance of interviewers falling into one of two equally defiant camps: do not show up to behavioral rounds, or show up and just check the boxes.

Thematically, questions center on diversity and inclusion, ambiguity, lessons learned from previous experience, and hard technical challenges.

In line with software engineering roles at Google, behavioral rounds for junior and mid-level engineers carry less weight. However, once you get to staff roles and above, the behavioral round becomes more of a priority.

Sample questions include:

Tell me about a time you disagreed with someone and how you resolved it.

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• Describe a time you used your values to ensure a diverse team and how you made sure everyone was included.
• A customer says [Issue], what would you do?
• What was your journey into data?

SQL

SQL is probably the most difficult round of the data engineering interview loop at Google. Expect medium/hard-level SQL questions. An added twist to SQL questions at Google is that you may have to explain your approach.

For example, you might be asked, “Why did you choose that JOIN?” and will need to tell them why it’s more optimal than alternatives. These questions are centered on learning your approach to query tuning and performance comparisons.

Topics to study:

• Joins
• Unions
• Window functions
• Subqueries
• Recursive queries
• CTEs
• Recursive CTEs
• Cyclic queries
• Gaps & Island Problems

Sample questions include:

• Top Salaries by Department
• Write a query to report the median of a user's searches, rounding the median to one decimal point.
• Remove all duplicate email addresses from a given list.

Coding

Data engineering loops contain easier problems than software engineering. Expect easy/medium-level data structures and algorithm problems. You might also get some Python data manipulation questions.

Topics to study:

• Graphs & Trees
• Strings
• Lists
• Recursion

Sample questions include:

• Generate N random numbers, insert them into a new array, and have the final array be sorted.
• How would you implement a binary search algorithm?
• In a given array of numbers, if a number is Even, divide by 2. If a given number is Odd, multiply by 3 and add 1.
• Which sorting algorithms use divide and conquer?

Data management

This round will ask you about data modeling, data warehousing, and data on the cloud. These rounds can be open-ended prompts asking you to design data marts. Another pattern is open-ended trivia-style questions on themes of big data and the cloud.

Topics to study:

• Big data tech and cloud infrastructure
• Advanced use of Google Cloud Platform tools like BigQuery and Dataflow

Sample questions include:

• How would you back up millions of records?
• When is Hadoop better than PySpark?
• How do you integrate data from multiple systems?
• Design a database for a stand-alone fast-food restaurant. Based on a database schema, write an SQL query to find the top three highest revenue-generating items sold the previous day.

Additional Resources

• Take our course to level up your data engineering skills.
• Get ready for Google’s coding-heavy process with our coding course.
• Get coaching from a data engineer at Google. With actionable feedback, you learn more quickly.
• Practice with mock interviews on the most common types of problems.

FAQs

How should I prepare for a Google Data Engineer interview?

Grind medium/hard SQL and easy/medium coding questions. Study up on cloud-related tech like BigQuery and Dataflow.

How much do Google Data Engineers make?

At Google, the data engineering levels and average total compensation look like:

• L3 (junior): $169k
• L4 (mid-level): $236k
• L5 (senior): $282k
• L6 (staff): $350k

How long is the Google Data Engineer interview process?

Google’s process tends to take longer than most companies. The average candidate finishes in 6–10 weeks.