Looker Interview Questions And Answers ( 2023 Updated)

Looker is a powerful business intelligence and data exploration platform that allows organizations to easily analyze and visualize their data. With Looker, users can create interactive dashboards, reports, and charts to gain insights and make data-driven decisions. As such, Looker has become a popular tool for data analysts, data engineers, and other data-minded professionals.

If you’re looking to land a job working with Looker, you’ll likely be asked a number of questions during the interview process to assess your knowledge of the tool and your ability to use it effectively. Here are some common Looker interview questions you might encounter:

We will learn best looker interview questions with expert answers in this blog.

Top 5 Looker Interview Questions

  • Can you explain the difference between a Look and a dashboard in Looker?

A Look is a single query or data set, while a dashboard is a collection of Looks and visualizations that can be organized and displayed together.

  • How would you go about creating a calculated field in Looker?

To create a calculated field in Looker, you would use the SQL editor to write a SQL expression that defines the calculation you want to perform. The new field can then be added to your data model and used in your analyses and visualizations.

  • Can you describe a scenario in which you used Looker to troubleshoot a problem?

In this question, the interviewer wants to know if you have experience using Looker to identify and solve real-world issues. This can be any scenario where you used Looker to investigate an issue, such as a discrepancy in data or a performance bottleneck.

  • How would you optimize a slow-performing Look in Looker?

There are a number of ways to optimize slow-performing Looks in Looker, such as by indexing the relevant fields, simplifying the query, or reducing the amount of data being pulled. The best approach will depend on the specific problem and the data involved.

  • How do you use the explore feature in Looker?

Explores allow users to query and filter data from a specific table, and users can create Looks from that data, as well as create charts, pivot tables, and other visualizations from the data returned by the query. You can also join different tables and perform calculations on the data.

In addition to these Looker questions, you may be asked about your experience with SQL, data modeling, and data visualization, as these are all closely related to using Looker. Looker is acquired by Google , and now it is the best time to learn it.

It’s important to be prepared to answer questions that explore your understanding of data and your ability to think critically and creatively. Being able to provide specific examples of how you’ve used Looker in the past to solve real-world problems will also be very helpful.

Looker Advanced Interview Questions

Here are some advanced Looker interview questions that you may be asked if you have more experience with the tool:

  • Can you explain the difference between a dimension and a measure in Looker’s data modeling?

Dimensions are data fields that are used to categorize and group data, while measures are data fields that are used to quantify and aggregate data. For example, a dimension might be “customer,” while a measure might be “sales.”

  • How do you use the Looker API to automate report generation or data extraction?

Looker API allows for automating many tasks, such as scheduling reports, fetching data, or triggering action. You can use the API to create a script that automatically generates a report or extracts data from Looker on a regular basis and save it in a desired format, such as CSV, or even post-process it with another tool.

  • How would you create a dynamic filter in Looker?

Dynamic filters allow users to filter data based on user-specified values. To create a dynamic filter, you would need to create a parameter in the data model, which can be used to filter the data in the queries. You can then add a form input element to your Look or dashboard that allows users to specify the value they want to filter on.

  • How would you optimize a Look that uses multiple subqueries?

Multiple subqueries can slow down performance in Looker, one way to optimize is to use a LookML join instead of subqueries, because joins are often more efficient than multiple subqueries. Additionally, you can also try to simplify the subqueries and remove unnecessary calculations or filters.

  • How would you use Looker’s data pipeline feature?

Looker’s data pipeline feature allows you to create a series of steps that will automatically transform and shape your data as it flows into Looker. You can use the data pipeline feature to clean and standardize data, perform calculations, and create new fields. This can be useful for automating the data preparation process, so that your data is ready for analysis as soon as it reaches Looker.

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