Table of Contents

Introduction

Google LLC. is an American tech giant that offers industry-based solutions. As a company that prides itself in “providing access to the world's information in one click”, Google offers a long list of products and services, including a huge hardware portfolio, an internet search engine, web email services, software solutions, web analytics, and AI.

As evidenced by their heavy investments in data and data infrastructure, Google understands the importance of data towards understanding customers' needs and driving business growth.

The Data Analyst Role at Google

Data analysis from Pixabay

Generally, the data analyst role requires retrieving, gathering, and organizing data from various data sources, in addition to using this information to provide meaningful insights towards business decisions. The role varies depending on the type of data collected, as well as the project type.

At Google, data analysts encompass all these duties and more. The position calls for analysts to not just understand the quantitative aspect of data analysis, but also the business impacts and how each indicator affects Google’s bottom line. As such, analysts at Google will also work closely with marketing teams to analyze and report performance index across Google’s products and services, partnering with engineering teams to create high-performance quantitative business models from Google’s internal data, and communicating relevant business findings to all levels of management.

Interested in becoming a data analyst at a company similar in size to Google? Check out "The Amazon Data Analyst Interview" on Interview Query!

Required Skills

Data analyst roles at Google require high-level field qualifications and extensive industry experience, setting a very high standard for hiring. They only take on qualified candidates with at least three years of industry experience in data analysis or related quantitative fields.

Other relevant criteria include:

  • Bachelor’s/Master’s in Computer Science, Math, Engineering, Economics, Finance, or equivalent practical experience.
  • Deep understanding of time series analysis, SQL, data warehousing, data modelling, ETL, dashboard automation and reporting.
  • Experience with data visualization tools such as Python, R, and Tableau.
  • Experience with Scripting languages (Javascript, Python, etc) and deep understanding of more advanced data science techniques and methodologies (Machine Learning, R, etc).
  • Experience translating analysis results into business recommendations and business questions into an analysis framework.

Data Analyst Teams at Google

Google cloud from Unsplash

Unlike business analysts, data analysts at Google generally focus more on data– specifically its collection, analysis, visualization, and presentation– to provide actionable insights and solutions for informed business decisions.

General skill requirements for this role include background knowledge in maths, statistics, programming (SQL, Python, etc), and industry-level experience with data manipulation and analytics.

Roles, irrespective of assigned teams, are analytics heavy. Specific positions depend on the assigned team and the type of projects assigned. However, the general data analyst role at Google ranges from basic analytics, modelling, visualization, and data presentation, to light machine learning techniques.

Some examples of specific roles in teams include:

Waze: Analysts develop and automate reports, perform extensive data analysis for business recommendations, and deliver effective presentations of findings to multiple levels of stakeholders. They also collaborate cross-functionally with Google’s internal clients to understand their business needs and formulate end-to-end analysis that includes data collection, analysis, existing scaled deliverables, and presentations.

Google Cloud: Data analysts on this team work to leverage Google’s big data to drive scalable analyses of business growth benchmarks and trends through a comprehensive scan of complex data. They also furnish “regional and function sales teams” with scalable insights and dashboards from this data.

People Analytics: Analysts on this team help drive data governance to ensure data integrity between different databases. They work on data modelling, develop and define metrics, perform prototyping, and generate business insights through advanced business intelligence techniques. They also manage data visualization and present insights for multi-level stakeholders in a clear and compelling manner.

Trust and Safety: Roles include performing large-scale analysis and modelling to identify opportunities for improvement, building dashboard reports and high-value, automated business intelligence solutions, and developing key performance indicators to monitor growth.

EMEA Sales Analytics team: Data analysts on this team leverage Google's big data to drive analysis at scale for business growth, performing sophisticated analysis that translates to actionable insights, and partnering with sales management to build scalable datasets, systems, dashboards, and analysis that systematically empowers sales organization.

Looking for another data analyst position that works on many different teams and is extremely cross-functional? Read "The Facebook Data Analyst Interview!

The Interview Process

Person looking at data charts from Unsplash

The Google Data Analyst interview process follows Google’s standard technical interview procedure. It starts with an initial phone interview with HR or a hiring manager. Candidates then proceed to the onsite interview, comprising three one-on-one interview rounds with a lunch break in between.

Like all Google hiring procedures, the interview will consist of high-quality questions that are tailored specifically to the position and the famous four Google attributes.

To better familiarize yourself with Google's interview process, we reccomend reviewing "The Google Business Analyst Interview" article on Interview Query!

Initial Phone Call

The initial phone call for the Google data analyst interview is similar to all of Google’s standard HR interviews. Here, the interviewer will ask exploratory questions aimed at getting to know more about you, your interests, your past project experiences relevant to the position, and your skillset as it relates to the job role on the team. The interviewer will also tell you about Google, its culture, the team you are applying for, and the scope of the job role in terms of expected capabilities.

Onsite Interview

Google’s data analyst onsite interview consists of three to four interviews, each lasting approximately 45 minutes, with a hiring manager, team manager, and a developer (to determine your SQL and data analytics skills). In between these interview rounds is a lunch break, where candidates get to informally talk with a current Googler.

In general, the Google data analyst onsite interview is a mixture of technical (standard SQL and statistics), product-sense (key metric definition), and culture-fit/behavioural assessments.

Notes and Tips

Google utilizes standardized questions on all its hiring interviews. For the data analyst role, candidates can expect questions on statistics (especially Central Limit theorem, Bayes theorem, conditional and joint probability, and basic distributions like exponential, geometric, and binomial distributions), experimental design, as well as lots of SQL (basic SQL queries, prioritization, joins, aggregations, filtering, etc,) and Python questions.

Remember to brush up on your knowledge of statistical and probability concepts, including regression, hypothesis testing, maximum likelihood estimation, and sampling.

For the SQL and Python portions, visit Interview Query and practice lots of Google data analyst SQL and Python questions.

Google Data Analyst Interview Questions

  • How would you compare the performance of two search engines?
  • What is your favorite Google product?
  • What was your work like in your former organization?
  • Tell me about yourself.
  • How do you design an online social network platform that follows all community guidelines?
  • Describe a really difficult project and tell me how you solved it.
  • Describe any data cleansing techniques you have used.