Looker Data Engineer Interview Questions + Guide in 2025

Looker is a powerful business intelligence and data analytics platform that enables organizations to explore and visualize their data, helping them make informed decisions based on actionable insights.

The Data Engineer role at Looker involves designing, building, and maintaining scalable data pipelines to support data analytics and business intelligence initiatives. Key responsibilities include developing and optimizing SQL queries, integrating data from various sources, and ensuring data quality and accessibility for analysts and stakeholders. A successful candidate will possess strong SQL skills, experience with data warehousing solutions, and a deep understanding of data modeling and ETL processes. Additionally, a collaborative mindset and the ability to communicate technical concepts effectively are essential traits that align with Looker's emphasis on teamwork and data-driven decision making.

This guide will empower you to prepare effectively for your interview by focusing on the specific skills and experiences that Looker values in its Data Engineers.

The interview process for a Data Engineer at Looker is structured to assess both technical skills and cultural fit within the company. It typically consists of several key stages:

1. Initial Phone Screen

The process begins with an initial phone screen, usually lasting about 30 minutes. During this conversation, a recruiter will discuss the role and the company culture, while also delving into your background, skills, and experiences. This is an opportunity for you to showcase your understanding of data engineering principles and how they align with Looker's mission.

2. Technical Assessment

Following the initial screen, candidates will undergo a technical assessment, which may be conducted via a video call. This assessment focuses heavily on SQL and data manipulation skills, as these are crucial for the role. Expect to solve practical problems that demonstrate your ability to analyze data effectively, particularly using Looker’s tools and methodologies. You may also be asked to discuss your previous projects and how you approached data challenges.

3. Onsite Interviews

The final stage typically involves onsite interviews, which may consist of multiple rounds with different team members. Each round will focus on various aspects of data engineering, including data modeling, ETL processes, and performance optimization. Additionally, you can expect behavioral questions that assess your problem-solving abilities and how you work within a team. Each interview is designed to evaluate both your technical expertise and your fit within Looker's collaborative environment.

As you prepare for these interviews, it's essential to familiarize yourself with the types of questions that may arise during the process.

Here are some tips to help you excel in your interview.

Understand Looker's Product and Ecosystem

Familiarize yourself with Looker’s data analytics platform and how it integrates with various data sources. Understanding the nuances of Looker’s capabilities, such as its modeling language (LookML) and how it supports data visualization and business intelligence, will give you an edge. Be prepared to discuss how you can leverage Looker to solve data challenges and enhance decision-making processes.

Master SQL and BI Tools

Since this role is heavily focused on SQL and business intelligence, ensure you are well-versed in SQL queries, particularly those that involve complex joins, aggregations, and window functions. Practice common SQL problems and familiarize yourself with Looker’s specific functionalities. Additionally, brush up on other BI tools and how they compare to Looker, as this knowledge can demonstrate your broader understanding of the data landscape.

Prepare for Technical Assessments

Expect to encounter technical assessments that may include coding challenges or case studies related to data engineering. Be ready to showcase your problem-solving skills and your ability to work with large datasets. Practicing LeetCode-style SQL problems can be beneficial, as they often reflect the types of challenges you may face in the role.

Embrace the Company Culture

Looker values innovation, collaboration, and a data-driven mindset. During your interview, convey your enthusiasm for working in a team-oriented environment and your commitment to leveraging data to drive business outcomes. Share examples from your past experiences that highlight your ability to collaborate effectively and contribute to a positive team dynamic.

Manage Expectations Post-Interview

Be prepared for the possibility of limited feedback after your interview. While this can be frustrating, focus on what you can control: your preparation and performance during the interview. Use this opportunity to ask insightful questions about the team and the role, which can help you gauge if Looker is the right fit for you.

By following these tips and tailoring your approach to Looker’s specific needs and culture, you can position yourself as a strong candidate for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Looker. The role will require a strong foundation in SQL, data modeling, and business intelligence concepts, as well as the ability to analyze and manipulate data effectively. Candidates should be prepared to demonstrate their technical skills and problem-solving abilities.

SQL and Data Manipulation

1. Can you explain the difference between INNER JOIN and LEFT JOIN?

Understanding the nuances of SQL joins is crucial for data manipulation and retrieval.

How to Answer

Clearly articulate the differences in how these joins function, providing examples of when each would be used.

Example

“An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table and the matched rows from the right table. For instance, if I have a table of customers and a table of orders, an INNER JOIN would show only customers who have placed orders, whereas a LEFT JOIN would show all customers, including those who haven’t placed any orders.”

2. How would you optimize a slow-running SQL query?

Performance optimization is key in data engineering roles, especially when dealing with large datasets.

How to Answer

Discuss various strategies such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I might add indexes to frequently queried columns, rewrite the query to reduce complexity, or break it into smaller parts to improve performance. For example, I once improved a report generation query by adding indexes and restructuring it, which reduced the execution time from several minutes to under 30 seconds.”

3. What are window functions in SQL, and when would you use them?

Window functions are essential for performing calculations across a set of table rows related to the current row.

How to Answer

Explain what window functions are and provide scenarios where they are beneficial.

Example

“Window functions allow you to perform calculations across a set of rows that are related to the current row, without collapsing the result set. I often use them for running totals or moving averages. For instance, in a sales report, I could use a window function to calculate the cumulative sales for each month while still displaying individual monthly sales.”

4. Describe a situation where you had to clean and prepare data for analysis.

Data cleaning is a critical part of the data engineering process.

How to Answer

Share a specific example that highlights your approach to data cleaning and the tools you used.

Example

“In a previous project, I was tasked with preparing a dataset for analysis that contained numerous missing values and inconsistencies. I used Python and Pandas to identify and fill missing values, standardize formats, and remove duplicates. This process ensured that the data was reliable and ready for analysis, ultimately leading to more accurate insights.”

5. How do you ensure data quality in your projects?

Maintaining data quality is vital for any data engineering role.

How to Answer

Discuss the methods and practices you implement to ensure data integrity and accuracy.

Example

“To ensure data quality, I implement validation checks at various stages of the data pipeline. This includes using automated tests to check for anomalies, setting up alerts for data discrepancies, and regularly reviewing data sources for accuracy. For instance, I once established a monitoring system that flagged any data that fell outside expected ranges, which helped catch errors early in the process.”

Data Modeling and Architecture

1. What is normalization, and why is it important in database design?

Normalization is a fundamental concept in database design that helps reduce redundancy.

How to Answer

Explain the normalization process and its benefits in maintaining data integrity.

Example

“Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves dividing large tables into smaller, related tables and defining relationships between them. For example, in a customer database, I would separate customer information from order details to avoid duplication and ensure that updates to customer data are consistent across the system.”

2. Can you describe a star schema and its advantages?

Understanding data modeling techniques is essential for effective data organization.

How to Answer

Define a star schema and discuss its benefits in data warehousing.

Example

“A star schema is a type of database schema that consists of a central fact table surrounded by dimension tables. This design simplifies queries and improves performance, making it easier to analyze large datasets. For instance, in a sales data warehouse, the fact table might contain sales transactions, while dimension tables could include product details, customer information, and time periods, allowing for efficient reporting and analysis.”

3. How do you approach ETL processes?

ETL (Extract, Transform, Load) processes are crucial for data integration.

How to Answer

Discuss your methodology for designing and implementing ETL processes.

Example

“I approach ETL processes by first understanding the data sources and the requirements for the target system. I then design the extraction process to pull data efficiently, followed by transformation steps to clean and format the data. Finally, I load the data into the target system, ensuring that the process is automated and monitored for any issues. For example, I once implemented an ETL pipeline that reduced data processing time by 50% through efficient extraction and transformation techniques.”

4. What tools and technologies have you used for data engineering?

Familiarity with various tools is essential for a data engineering role.

How to Answer

List the tools you have experience with and how you have used them in your projects.

Example

“I have experience with a variety of data engineering tools, including Apache Spark for big data processing, Airflow for orchestrating workflows, and Looker for data visualization. In my last role, I used Spark to process large datasets in real-time, which significantly improved our data analysis capabilities and allowed for more timely insights.”

5. How do you handle schema changes in a production database?

Schema changes can impact data integrity and application performance.

How to Answer

Explain your approach to managing schema changes while minimizing disruption.

Example

“When handling schema changes in a production database, I follow a careful process that includes planning, testing, and communication. I first assess the impact of the change and create a rollback plan. Then, I implement the change during off-peak hours and monitor the system closely for any issues. For instance, I once successfully migrated a database schema to accommodate new data requirements without any downtime by thoroughly testing the changes in a staging environment first.”