In this comprehensive guide, we will cover the top 100 interview questions for a Looker Developer position. These questions span topics such as LookML, SQL, data modeling, dashboard creation, performance optimization, and integration with APIs. The answers provided will help you prepare for a successful interview and showcase your expertise as a Looker Developer.
General Looker Developer Interview Questions
1. What is Looker, and how does it differ from other BI tools?
Answer: Looker is a cloud-based business intelligence (BI) tool that allows users to explore, analyze, and visualize data in real-time. Unlike other BI tools like Tableau or Power BI, Looker uses LookML, a modeling language that simplifies SQL queries and allows for scalable data modeling. Looker is tightly integrated with cloud-based data warehouses, making it highly effective for handling large datasets.
2. What is LookML, and why is it important in Looker?
Answer: LookML (Looker Modeling Language) is Looker’s proprietary language used to define data models, such as dimensions, measures, and relationships between datasets. It abstracts SQL into reusable components, allowing developers to build efficient and scalable data models that power Looker dashboards and reports.
3. What are the key components of LookML?
Answer: The key components of LookML include:
- Model: Defines how Looker should interact with the underlying database.
- View: Describes the data within a specific table or dataset.
- Explore: Provides users with an interface to query data.
- Dimension: Represents individual data fields.
- Measure: Defines aggregations like sums, averages, and counts.
- Join: Specifies relationships between tables in the data model.
4. What is a Looker Explore?
Answer: A Looker Explore is an interface that allows users to interact with data and run ad-hoc queries. It enables users to filter, group, and visualize data without writing SQL manually. Explores are based on LookML models, which define how data is structured and how tables are related.
5. How does Looker handle data security?
Answer: Looker provides robust data security features, including row-level security, role-based access control (RBAC), and data encryption. Developers can define access permissions based on roles, ensuring that only authorized users can view or modify specific data. Looker also supports OAuth, SAML, and multi-factor authentication (MFA) for secure access.
LookML-Specific Questions
6. What are dimensions and measures in LookML?
Answer:
- Dimensions: Fields that users can group by or filter on in an Explore. For example, a customer’s name or a product category.
- Measures: Aggregated values such as sums, averages, or counts. For example, total sales or the average order value.
7. How do you define a dimension in LookML?
Answer: A dimension is defined within a view in LookML. For example:
dimension: product_name {
type: string
sql: ${TABLE}.product_name ;;
}
8. How do you define a measure in LookML?
Answer: A measure is defined within a view in LookML. For example:
measure: total_revenue {
type: sum
sql: ${TABLE}.revenue ;;
}
9. What are the different types of joins in LookML?
Answer: The different types of joins in LookML are:
- Inner Join
- Left Join
- Right Join
- Full Outer Join
10. How do you define a join in LookML?
Answer: A join is defined in the model file, specifying how two tables are related:
join: orders {
sql_on: ${users.user_id} = ${orders.user_id} ;;
relationship: one_to_many
}
11. What is a derived table in LookML?
Answer: A derived table is a temporary table generated by a custom SQL query within LookML. It allows developers to create complex logic or perform aggregations not directly available in the base tables.
12. How do you create a derived table in LookML?
Answer:
derived_table: {
sql: SELECT customer_id, COUNT(order_id) AS order_count
FROM orders
GROUP BY customer_id ;;
}
13. What are Persistent Derived Tables (PDTs), and why are they important?
Answer: PDTs are precomputed and stored derived tables in the database, used to improve query performance for frequently queried datasets. They reduce the load on the database by caching the results of complex queries.
14. What is aggregate awareness in LookML?
Answer: Aggregate awareness is a Looker feature that allows developers to define multiple levels of aggregation for the same data, optimizing performance. Looker will use the most efficient level of aggregation based on the user’s query.
15. How do you optimize LookML for performance?
Answer:
- Use PDTs for expensive queries.
- Optimize SQL queries with indexes and joins.
- Limit the number of fields in Explores.
- Use derived tables for complex calculations.
- Enable caching for frequently accessed data.
SQL for Looker Developers
16. What is SQL, and why is it important for a Looker Developer?
Answer: SQL (Structured Query Language) is essential for querying relational databases. LookML translates user queries into SQL, so understanding SQL helps developers optimize LookML models and troubleshoot issues.
17. How would you write a basic SQL query to retrieve customer names and their order totals?
Answer:
SELECT customers.customer_name, SUM(orders.order_total)
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id
GROUP BY customers.customer_name;
18. What are window functions in SQL, and when would you use them in Looker?
Answer: Window functions perform calculations across a set of table rows that are related to the current row. These are useful for running totals, moving averages, and ranking without needing to group data. In Looker, they can be used for complex calculations in derived tables.
19. How would you optimize a slow-running SQL query?
Answer:
- Use indexes on frequently queried columns.
- Avoid **SELECT *** and only retrieve the necessary columns.
- Minimize joins and ensure they are efficient.
- Use subqueries or CTEs to simplify logic.
20. Explain the difference between INNER JOIN and LEFT JOIN in SQL.
Answer:
- INNER JOIN returns only matching rows from both tables.
- LEFT JOIN returns all rows from the left table and matching rows from the right table, with NULLs for non-matching rows.
21. What is a CTE (Common Table Expression), and how is it used?
Answer: A CTE is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It’s useful for breaking down complex queries into more manageable parts.
22. What is a GROUP BY clause, and when would you use it?
Answer: The GROUP BY clause is used to group rows that have the same values in specified columns. It’s often used with aggregate functions like SUM, COUNT, or AVG.
23. How do you handle NULL values in SQL?
Answer: You can use the COALESCE() function to replace NULL values with a default value. For example:
SELECT COALESCE(customer_name, ‘Unknown’) FROM customers;
Looker Dashboard and Visualization Questions
24. How do you create a dashboard in Looker?
Answer: To create a dashboard, you:
- Build an Explore or query.
- Save the visualization.
- Add the visualization to a new or existing dashboard.
- Customize the layout, filters, and interactivity of the dashboard.
25. How do you add filters to a Looker dashboard?
Answer: Filters can be added by selecting “Add Filter” in the dashboard editing mode. You can then apply filters to one or more tiles and choose which fields to filter on (e.g., date range, region, product category).
26. What are drill-downs in Looker, and how do they work?
Answer: Drill-downs allow users to click on a data point in a visualization and view more detailed data related to that point. Drill-downs can be defined in LookML by specifying additional dimensions to display when a user clicks on a value.
27. How do you optimize a dashboard for performance?
Answer:
- Use PDTs for expensive queries.
- Limit the number of visualizations and filters on the dashboard.
- Use caching for frequently accessed reports.
- Minimize the use of joins in LookML.
28. What are Looker tiles?
Answer: A tile in Looker represents a single visualization, such as a chart or table, on a dashboard. Multiple tiles can be added to a dashboard to create a comprehensive view of the data.
Advanced LookML Questions
29. How do you create a custom dimension using SQL in LookML?
Answer:
dimension: custom_dimension {
sql: CASE
WHEN ${TABLE}.revenue > 10000 THEN ‘High’
ELSE ‘Low’
END ;;
}
30. How do you handle large datasets in Looker?
Answer:
- Use aggregate awareness to reduce the amount of data being queried.
- Utilize PDTs for precomputing large datasets.
- Optimize LookML models to reduce the number of joins and simplify queries.
31. What are the best practices for structuring LookML projects?
Answer:
- Keep models and views in separate files for better organization.
- Use consistent naming conventions for dimensions, measures, and explores.
- Document complex queries and logic within LookML code.
- Use Git for version control and collaboration.
Looker API and Integration Questions
32. What is the Looker API, and how is it used?
Answer: The Looker API allows developers to interact with Looker programmatically. It can be used to retrieve data, automate reports, manage dashboards, and embed Looker content into external applications.
33. How would you retrieve data from Looker using the API?
Answer: You can use the Looker API to run a query and retrieve data:
curl -X GET “https://.api.looker.com/api/3.1/query/12345/run/json” \
-H “Authorization: token “
34. How can you embed Looker dashboards in an external application?
Answer: Looker dashboards can be embedded using the Looker Embed API or via an iframe. The Embed API allows for secure, interactive embeds with customized permissions.
Performance Optimization Questions
35. What techniques do you use to optimize query performance in Looker?
Answer:
- Use Persistent Derived Tables (PDTs) for complex queries.
- Simplify joins and use indexing in the database.
- Leverage aggregate awareness to reduce data load.
- Enable caching for frequently accessed data.
36. How do you monitor performance in Looker?
Answer: Looker provides a System Activity dashboard that shows the performance of queries, load times, and database usage. Developers can use this dashboard to identify bottlenecks and optimize queries or data models.
Business-Focused Questions
37. How do you ensure that Looker dashboards align with business needs?
Answer: Regularly collaborate with business stakeholders to understand their key performance indicators (KPIs) and reporting needs. Use feedback loops and iteration to ensure dashboards are both accurate and actionable.
38. How do you handle conflicting data requirements from different teams?
Answer: Discuss and prioritize requirements with the stakeholders involved, and ensure that data models are flexible enough to serve multiple purposes. Implement filters or different dashboards to meet specific team needs.
Git and Version Control in LookML
39. How do you use Git with Looker?
Answer: Looker integrates with Git for version control. Developers can use Git branches to manage changes in LookML models, collaborate with other team members, and ensure a stable production environment.
40. How do you handle conflicts in LookML when using Git?
Answer: Use Git commands like git merge to handle conflicts, and manually resolve any discrepancies in the LookML code. Looker also provides visual cues in the development environment to help resolve conflicts.
Looker Developer Scenario-Based Questions
41. How would you create a sales dashboard that shows year-over-year growth?
Answer:
- Use LookML to define total sales as a measure.
- Create a custom dimension that calculates the difference between this year’s sales and last year’s sales using a derived table.
- Build a dashboard with a line chart showing sales over time and a bar chart for YoY growth.
42. A user reports that a dashboard is showing incorrect data. How would you troubleshoot?
Answer:
- Check the LookML model for errors in the definitions of dimensions and measures.
- Verify the underlying SQL queries generated by LookML to ensure the correct data is being pulled.
- Review the data pipeline to ensure that data is being loaded correctly into the data warehouse.
43. How would you design a dashboard for non-technical users?
Answer:
- Use simple visualizations such as bar charts, line graphs, and KPIs.
- Add filters and drill-downs for interactivity.
- Provide tooltips and labels for clarity and ensure the dashboard is intuitive and easy to use.
More Advanced Looker Developer Questions
44. What is a sub-explore in Looker?
Answer: A sub-explore allows users to drill into related data within an Explore. For example, from an Explore showing customer orders, a user can click on a customer’s name to view a sub-explore with details about that specific customer’s purchases.
45. How do you implement row-level security in Looker?
Answer: Row-level security can be implemented by adding conditions to LookML models that restrict data access based on the user’s role or group. For example:
access_filter: {
field: region
user_attribute: user_region
}
46. What is a fact table and a dimension table?
Answer:
- Fact Table: A table that contains transactional or quantitative data, such as sales orders.
- Dimension Table: A table that contains descriptive information, such as customer names or product categories, which can be used to filter or group fact data.
47. How do you schedule a report in Looker?
Answer: Looker allows you to schedule reports via email, Slack, or webhooks. You can specify the frequency (e.g., daily, weekly) and set the recipients. Reports can also be sent as CSV, Excel, or other formats.
48. What are Looker user attributes, and how are they used?
Answer: User attributes in Looker allow you to customize data access and dashboard experiences based on a user’s role, region, or preferences. They are often used in conjunction with row-level security to restrict access to certain data.
49. How do you enable caching in Looker?
Answer: Caching in Looker is automatically enabled for frequently queried data. Developers can configure caching policies in the LookML model using the persist_for parameter in PDTs or the caching settings for specific Explores.
50. What are the advantages of using Looker with cloud data warehouses like BigQuery or Snowflake?
Answer: Looker’s deep integration with cloud data warehouses allows for real-time data analysis, scalability, and cost-efficiency. Cloud data warehouses handle large datasets efficiently, and Looker’s LookML abstracts complex queries into reusable components, making it easier to manage and query big data.
Advanced Looker Developer Questions (51-100)
51. What are the key performance considerations when using joins in LookML?
Answer: Minimize the number of joins, ensure indexes are used in the database, and avoid joining unnecessarily large tables.
52. What is aggregate persistence in Looker, and how does it work?
Answer: Aggregate persistence allows developers to store pre-aggregated data, reducing query load by using the stored results instead of re-running complex calculations each time.
53. How would you ensure data governance in Looker?
Answer: Implement role-based access control (RBAC), use row-level security for sensitive data, and ensure all data models comply with company data policies and industry regulations.
54. What is the difference between a full refresh and incremental loading for PDTs?
Answer:
- Full Refresh: Rebuilds the entire PDT every time the table is refreshed.
- Incremental Loading: Only updates the PDT with new or changed data, reducing the load on the database.
Scenario-Based Questions for Looker Developers (Continuing)
55. How would you handle duplicate records in a dataset in Looker?
Answer: Use a DISTINCT clause in SQL queries or apply filters in LookML to ensure only unique records are used in calculations.
56. Explain the importance of using Git in Looker projects.
Answer: Git allows for version control, collaboration, and rollback in Looker projects, ensuring code integrity and enabling multiple developers to work on the same project without conflicts.
57. How do you implement dynamic dimensions and measures in LookML?
Answer: Dynamic dimensions and measures can be implemented using liquid variables or user attributes in LookML. This allows the definition of dimensions or measures to change based on user input or preferences.
58. What are liquid variables, and how are they used in LookML?
Answer: Liquid variables are embedded templating syntax that Looker uses to dynamically change SQL based on user inputs. They are often used to create conditional logic or dynamic SQL statements in LookML.
59. How do you handle missing or null values in LookML?
Answer: Use the COALESCE() function in SQL to replace NULL values with a default value. For example, to replace null customer names with “Unknown,” you would write:
sql: COALESCE(${TABLE}.customer_name, ‘Unknown’) ;;
60. What are some best practices for building scalable LookML models?
Answer:
- Keep models organized and modular, separating large models into smaller, reusable components.
- Use consistent naming conventions.
- Use derived tables and PDTs only when necessary, and optimize SQL queries.
- Regularly document complex logic in LookML for maintainability.
- Utilize caching and performance optimization techniques.
61. How do you configure data access permissions in Looker?
Answer: Data access permissions in Looker can be managed through roles and groups, where permissions are applied to specific users or groups based on their role in the organization. You can also define row-level security in LookML to control access to specific rows of data based on user attributes.
62. Explain the concept of row-level security in Looker.
Answer: Row-level security ensures that users only see the rows of data they are authorized to access. This can be implemented using access filters in LookML based on user attributes or roles.
63. What is aggregate persistence in LookML, and when should you use it?
Answer: Aggregate persistence stores pre-aggregated results for large datasets. It reduces the need to re-run aggregations on every query, improving performance for dashboards and reports with heavy data usage. You should use it when dealing with very large datasets or complex queries that involve many calculations.
64. How do you manage and organize LookML code in larger projects?
Answer: Best practices for managing LookML code in larger projects include:
- Using Git for version control.
- Organizing models, views, and explores in separate files.
- Naming conventions to improve readability.
- Reusing code components wherever possible.
- Keeping functions modular and well-documented.
65. How do you handle multiple environments in Looker (development, testing, production)?
Answer: Looker integrates with Git to manage multiple environments. By using branches (e.g., development, testing, and production), developers can work on new features in the development environment, test changes in staging, and deploy only tested changes to production.
66. What is a subquery in SQL, and how would you use it in a LookML model?
Answer: A subquery is a query nested inside another SQL query. In LookML, subqueries are used in derived tables to perform complex calculations or filtering before data is made available for querying in the model. Example:
derived_table: {
sql: SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
WHERE order_date > ‘2023-01-01’
GROUP BY customer_id ;;
}
67. How do you troubleshoot a slow-running dashboard in Looker?
Answer:
- Check the SQL generated by LookML to identify performance bottlenecks.
- Optimize joins and reduce unnecessary data fields.
- Use Persistent Derived Tables (PDTs) for precomputed results.
- Minimize the number of visualizations and filters on the dashboard.
- Enable caching for frequently accessed data.
68. What is an explore, and how do you optimize it for user experience?
Answer: An explore is an interface that allows users to query data without writing SQL. To optimize an explore:
- Include only relevant dimensions and measures.
- Use concise field names and descriptions.
- Provide filters for common use cases.
- Optimize performance by limiting joins and data returned.
69. How does Looker’s cache work, and how can you control it?
Answer: Looker automatically caches query results to improve performance. Developers can control caching by setting persist_for parameters in LookML or adjusting cache settings for specific explores or dashboards.
70. How do you create a KPI (Key Performance Indicator) in Looker?
Answer: To create a KPI, define a measure in LookML that represents the metric you want to track (e.g., total revenue, average sales). You can then visualize this measure as a single value in a dashboard, often with color coding to indicate performance thresholds.
71. How do you handle data consistency and accuracy in Looker dashboards?
Answer:
- Ensure data validation by comparing LookML models with the source data.
- Collaborate with data engineers to ensure ETL processes deliver clean, consistent data.
- Regularly test LookML models for accuracy.
- Use persistent tables for calculated fields to reduce inconsistencies.
72. Explain how to create a drill-down in Looker.
Answer: Drill-downs allow users to click on a visualization or data point to view more detailed data. You can define drill fields in LookML for a dimension like this:
dimension: product_category {
sql: ${TABLE}.product_category ;;
drill_fields: [product_id, product_name]
}
73. How would you calculate a year-over-year growth percentage in LookML?
Answer: You can calculate year-over-year growth by defining a custom measure that compares sales or revenue from one year to the next:
measure: yoy_growth {
type: number
sql: (SUM(${TABLE}.current_year_sales) – SUM(${TABLE}.previous_year_sales)) / SUM(${TABLE}.previous_year_sales) ;;
}
74. What are Looker user attributes, and how can they be used to personalize dashboards?
Answer: User attributes allow you to customize user experiences based on their roles or preferences. For example, a dashboard could be filtered based on the user’s region by using a user attribute in a filter or row-level security logic.
75. What is the relationship between Looker and cloud data warehouses like BigQuery or Snowflake?
Answer: Looker connects directly to cloud data warehouses like BigQuery or Snowflake. It queries data in real-time, leveraging the cloud warehouse’s scalability and performance for large datasets. Looker does not store data but instead runs SQL queries on the connected data warehouse.
Scenario-Based Questions (Advanced)
76. A dashboard is showing incorrect data. How would you troubleshoot and resolve this issue?
Answer:
- First, review the LookML model to verify that dimensions and measures are defined correctly.
- Check the underlying SQL queries generated by LookML.
- Ensure the data pipeline (ETL) is functioning correctly and data is up-to-date.
- Validate the data in the source database to ensure the data itself is accurate.
- Test filters and parameters in the dashboard for incorrect application.
77. How would you implement row-level security for sales data that is region-specific?
Answer: You would use user attributes to filter data by region, allowing only the sales data for a specific region to be shown to users who are authorized to see it:
access_filter: {
field: region
user_attribute: user_region
}
78. How would you optimize a LookML model that has become slow due to multiple joins and large datasets?
Answer:
- Minimize the number of joins and only include necessary tables.
- Use Persistent Derived Tables (PDTs) to precompute expensive queries.
- Create aggregate tables to store pre-aggregated data.
- Limit the fields available in Explores to reduce complexity.
79. How would you build a real-time dashboard in Looker?
Answer: To build a real-time dashboard, connect Looker to a database that supports real-time data streaming, such as BigQuery with real-time tables. Ensure that caching is appropriately disabled, and use live queries to pull the latest data.
80. How do you ensure consistency across multiple dashboards built for different teams?
Answer:
- Use a consistent LookML model that serves as the foundation for all dashboards.
- Apply naming conventions for dimensions and measures.
- Build standardized dashboards with shared templates or Looker Blocks.
- Regularly review and update models to reflect business changes.
81. How do you calculate a moving average in LookML?
Answer: Use a window function in a derived table to calculate the moving average:
derived_table: {
sql: SELECT order_date, AVG(sales_amount) OVER (ORDER BY order_date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) AS moving_avg_sales
FROM sales
;;
}
82. How do you handle a dashboard that needs to display data from multiple time zones?
Answer: Convert all timestamps to a universal time zone (e.g., UTC) at the data ingestion level. Then, allow users to select their time zone using a parameter or user attribute to display the data appropriately.
83. How would you use the Looker API to schedule a report?
Answer:
- Use the Looker API to create a schedule job for a specific report:
curl -X POST “https://.api.looker.com/api/3.1/scheduled_plans” \
-H “Authorization: token ” \
-d ‘{
“look_id”: “123”,
“name”: “Weekly Sales Report”,
“run_once”: false,
“schedule”: {
“time”: “09:00”,
“timezone”: “America/Los_Angeles”,
“repeats”: “weekly”
},
“enabled”: true
}’
More Advanced Looker Developer Questions (84-100)
84. How do you ensure dashboard performance remains consistent as the underlying dataset grows?
Answer:
- Regularly update PDTs and aggregate tables to optimize performance.
- Reduce unnecessary joins and fields in LookML.
- Limit the number of visualizations on a dashboard.
- Implement caching strategies for frequently queried data.
85. How would you embed a Looker dashboard into an external web application?
Answer: Use the Looker Embed API to securely embed dashboards within a web application. You can define permissions and user access to ensure data security.
86. How do you manage user permissions for sensitive data in Looker?
Answer: Implement role-based access control (RBAC) and use row-level security to ensure that only authorized users can access sensitive data. You can also apply data masking for specific fields if necessary.
87. Explain the process of scheduling Looker reports via email or Slack.
Answer: Looker allows you to schedule reports via email or Slack by configuring a scheduled plan. You define the recipients, frequency, format (e.g., CSV or PDF), and delivery channel within the Looker UI.
88. How would you manage and organize LookML projects with a large team of developers?
Answer:
- Use Git for version control and collaboration.
- Adopt branching strategies to manage development, staging, and production environments.
- Keep LookML code modular and organized, using clear documentation and naming conventions.
89. What are the key differences between a derived table and a Persistent Derived Table (PDT)?
Answer:
- A derived table is a temporary table created at query time and only exists during that query.
- A PDT is a materialized table stored in the database, which can persist for a set amount of time, making it more efficient for recurring queries.
90. How would you handle incremental loading in a PDT?
Answer: Incremental loading allows you to update a PDT with only the new or changed data, rather than rebuilding the entire table. This can be done by adding a condition in the PDT’s SQL query to only include recent data.
Looker Developer Problem-Solving Scenarios
91. A client needs a custom report that includes calculated fields and dynamic filters. How do you implement this in Looker?
Answer:
- Create custom dimensions and measures in LookML for the calculated fields.
- Build a custom dashboard with interactive filters to allow users to adjust the report dynamically.
92. How would you implement multi-language support in a Looker dashboard?
Answer: Use parameters to allow users to select their language preference, then dynamically update field labels, filters, and text on the dashboard based on the selected language.
API and Integration Questions (93-100)
93. How would you automate Looker report delivery using the API?
Answer: Use the Looker API to create scheduled plans or run queries programmatically. You can use this to send reports automatically at specific intervals.
94. How would you embed a Looker visualization in a custom web app using the API?
Answer: Use the Looker Embed API to embed visualizations securely. Ensure that permissions and user roles are properly set to restrict access to sensitive data.
95. How can Looker integrate with external systems like Salesforce or Google Analytics?
Answer: Looker can connect to external data sources through its connectors or APIs. You can also use ETL tools to bring data from external systems into a data warehouse and then connect Looker to that warehouse for analysis.
96. How do you use the Looker API to retrieve query results in JSON format?
Answer:
- Call the Looker API to run a query and retrieve the results in JSON format:
curl -X GET “https://.api.looker.com/api/3.1/query/123/run/json” \
-H “Authorization: token “
97. How do you handle authentication when using the Looker API?
Answer: Authentication with the Looker API is handled through API tokens. You generate API credentials in the Looker Admin panel and use these tokens to authenticate API requests.
98. How would you create a real-time data integration between Looker and an external system using webhooks?
Answer: You can use Looker’s webhook integrations to send real-time data updates to external systems. For example, set up a webhook that triggers when new data is available, and send the data to a downstream system.
99. How do you manage Looker API rate limits?
Answer: Monitor API usage and implement strategies to avoid exceeding rate limits, such as batching API requests or reducing the frequency of calls.
100. What are some common use cases for the Looker API?
Answer: Common use cases for the Looker API include:
- Automating report generation and delivery.
- Embedding Looker dashboards and visualizations in custom applications.
- Managing user access and permissions programmatically.
- Retrieving and exporting data for integration with other systems.
Conclusion
By preparing for these top 100 Looker Developer interview questions, you’ll be well-equipped to demonstrate your expertise in LookML, SQL, data modeling, dashboard creation, and API integration. These questions cover everything from fundamental concepts to advanced problem-solving, helping you ace your Looker Developer interview and showcase your full range of skills.