Splunk Data Engineer Interview Questions + Guide in 2025

Splunk is a leader in creating a safer and more resilient digital environment, providing top enterprises with a unified security and observability platform to ensure the reliability of their digital systems.

As a Data Engineer at Splunk, you will play a crucial role in driving operational excellence and performance insights across the organization. This position involves collaborating with various teams, including data systems analysts, IT, Finance, Sales, and Customer Experience, to enhance data management strategies. Your core responsibilities will include designing and developing go-to-market analytics using tools like DBT, Snowflake, and Python, and ensuring the seamless integration of data into actionable insights to improve business performance.

To excel in this role, you will need a comprehensive understanding of data engineering principles, hands-on experience with cloud-based data warehouses, and proficiency in SQL and Python. Your analytical skills must be complemented by effective communication abilities, allowing you to engage with internal stakeholders and contribute significantly to Splunk’s growth. A keen attention to detail and an aptitude for identifying opportunities to optimize systems and processes will also set you apart as an ideal candidate.

This guide aims to help you prepare thoroughly for your interview at Splunk by providing insights into the role, the skills required, and the expectations from a successful Data Engineer within the company.

The interview process for a Data Engineer role at Splunk is designed to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative and innovative environment at the company. The process typically unfolds over several stages, allowing candidates to demonstrate their expertise and fit within the team.

1. Initial Phone Screen

The first step in the interview process is a phone screen with a recruiter or hiring manager. This conversation usually lasts around 30 minutes and focuses on your background, experience, and motivation for applying to Splunk. Expect to discuss your familiarity with data engineering concepts, tools, and your understanding of the company's mission and values.

2. Technical Assessment

Following the initial screen, candidates often undergo a technical assessment, which may be conducted via a video call. This round typically includes a mix of coding exercises and technical questions related to SQL, Python, and data engineering principles. You may be asked to solve problems in real-time, such as writing queries or discussing your approach to data orchestration and analytics.

3. Behavioral Interviews

Candidates will then participate in one or more behavioral interviews with team members and possibly a director. These interviews focus on your past experiences, problem-solving abilities, and how you work within a team. Expect questions that explore your approach to collaboration, handling challenges, and your ability to communicate complex ideas effectively.

4. Final Interview Round

The final round usually consists of multiple interviews with various stakeholders, including product owners and senior engineers. This stage may involve deeper technical discussions, case studies, or practical exercises that reflect the work you would be doing at Splunk. You may also be asked to present your previous projects or experiences that demonstrate your analytical skills and understanding of data management.

5. Feedback and Offer

After the interviews, candidates can expect a follow-up from the recruiter regarding the outcome. While feedback may not always be provided, the recruiter will communicate the final decision, whether it be an offer or a decline.

As you prepare for your interviews, it's essential to be ready for the specific questions that may arise during the process.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Splunk. The interview process will likely assess your technical skills in data management, analytics, and engineering, as well as your ability to collaborate with cross-functional teams. Be prepared to demonstrate your knowledge of SQL, Python, and cloud-based data solutions, as well as your problem-solving abilities in real-world scenarios.

Technical Skills

1. What metrics have you worked on in your previous roles?

This question aims to understand your experience with data metrics and how you have utilized them to drive business decisions.

How to Answer

Discuss specific metrics you have developed or analyzed, emphasizing their impact on business performance. Be sure to mention any tools or technologies you used.

Example

“In my previous role, I worked on customer engagement metrics, specifically tracking user interactions with our platform. I utilized SQL to extract data from our database and created dashboards in Tableau to visualize trends, which helped the marketing team tailor their campaigns effectively.”

2. How do you connect a database to a data analysis tool like Splunk?

This question assesses your understanding of data integration and the tools you use for connecting databases.

How to Answer

Explain the process of connecting a database to Splunk, including any specific configurations or tools you have used.

Example

“To connect a database to Splunk, I typically use the Splunk DB Connect app. I configure the connection by providing the database credentials and setting up the necessary queries to pull the required data into Splunk for analysis.”

3. Can you explain how you onboard data from different sources?

This question evaluates your experience with data ingestion and integration from various sources.

How to Answer

Describe your approach to data onboarding, including any challenges you faced and how you overcame them.

Example

“I onboard data from various sources by first assessing the data formats and structures. I then use ETL processes to transform and load the data into our data warehouse, ensuring data quality and consistency. For instance, I once integrated data from both SQL and NoSQL databases, which required careful mapping of data fields.”

4. What strategies do you use to restrict user access to specific data?

This question tests your knowledge of data security and user permissions.

How to Answer

Discuss the methods you employ to manage user access and ensure data security.

Example

“I implement role-based access control (RBAC) to restrict user access to sensitive data. By defining user roles and permissions, I ensure that only authorized personnel can access specific datasets, which helps maintain data integrity and security.”

5. Write a SQL query to hide all null values in a dataset.

This question assesses your SQL skills and ability to manipulate data.

How to Answer

Provide a clear SQL query that demonstrates your understanding of data filtering.

Example

“Here’s a SQL query that hides all null values from a dataset: SELECT * FROM my_table WHERE my_column IS NOT NULL; This query retrieves all records where my_column does not contain null values.”

Programming and Tools

1. Describe a project where you used Python for data orchestration.

This question evaluates your programming skills and experience with data orchestration.

How to Answer

Share a specific project where you utilized Python, detailing the tools and libraries you used.

Example

“I worked on a project where I used Python with Airflow to orchestrate data pipelines. I created DAGs that automated the extraction, transformation, and loading of data from various sources into our data warehouse, significantly reducing manual effort and improving data accuracy.”

2. How do you use DBT in your data engineering processes?

This question assesses your familiarity with DBT and its application in data transformation.

How to Answer

Explain how you leverage DBT for data modeling and transformation in your workflows.

Example

“I use DBT to manage data transformations by writing modular SQL scripts that define how raw data is transformed into analytics-ready datasets. This allows for better version control and collaboration among team members, as well as easier testing of data models.”

3. Can you explain your experience with cloud-based data warehouses like Snowflake?

This question evaluates your knowledge of cloud data solutions and their implementation.

How to Answer

Discuss your experience with Snowflake or similar platforms, focusing on specific features you have utilized.

Example

“I have extensive experience with Snowflake, particularly in leveraging its scalability and performance for large datasets. I have used Snowflake’s data sharing capabilities to collaborate with other teams, allowing for real-time access to data without duplicating it.”

4. What tools do you use for version control in your projects?

This question assesses your understanding of version control systems and their importance in data engineering.

How to Answer

Mention the version control tools you are familiar with and how you use them in your projects.

Example

“I primarily use GitLab for version control in my projects. It allows me to track changes in my code, collaborate with team members, and manage different branches for feature development, ensuring a smooth workflow.”

5. Write a SQL query to convert '6127 sq. feet' to numeric digits.

This question tests your SQL skills and ability to manipulate string data.

How to Answer

Provide a SQL query that demonstrates your ability to extract numeric values from a string.

Example

“Here’s a SQL query that converts '6127 sq. feet' to numeric digits: SELECT CAST(REPLACE('6127 sq. feet', ' sq. feet', '') AS INT); This query removes the text and converts the remaining string to an integer.”