ServiceNow Data Engineer Interview Questions + Guide in 2025

ServiceNow is a leading enterprise cloud company that transforms the way organizations manage their digital workflow by providing innovative solutions that enhance efficiency and productivity.

As a Data Engineer at ServiceNow, you will play a pivotal role in shaping the organization's data strategy and architecture. Your main responsibilities will include designing, building, and maintaining scalable data pipelines and storage solutions to support the company's objectives. You will be expected to collaborate closely with cross-functional teams, translating complex business requirements into reliable data models and ensuring data quality and integrity. Proficiency in SQL and experience with various data management tools and cloud technologies are essential for this role. The ideal candidate will possess a strong analytical mindset, excellent problem-solving skills, and the ability to communicate effectively with both technical and non-technical stakeholders.

This guide aims to equip you with the insights and preparation needed to excel in your interview for the Data Engineer role at ServiceNow by understanding the nuances of the position and the company's expectations.

The interview process for a Data Engineer role at ServiceNow is structured to assess both technical skills and cultural fit within the organization. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and experience.

1. Initial Screening

The process begins with an initial screening, usually conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, skills, and motivations for applying to ServiceNow. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role, ensuring that you have a clear understanding of what to expect.

2. Technical Screening

Following the initial screening, candidates typically undergo a technical screening. This may be conducted via video call with a technical lead or a senior data engineer. During this session, you can expect to discuss your technical expertise in data engineering, including your experience with data modeling, ETL processes, and relevant programming languages. Be prepared to solve problems on the spot, as this stage often includes practical assessments or coding challenges.

3. Onsite Interviews

The onsite interview process usually consists of multiple rounds, often five, each lasting around 30 minutes. These interviews are typically conducted by various team members, including data engineers, architects, and possibly product managers. The focus will be on both technical and behavioral questions. Expect to discuss your past projects, your approach to data architecture, and how you handle challenges in data management. Additionally, you may be asked to demonstrate your SQL skills through verbal explanations, which can be challenging without the ability to write out queries.

4. Final Interview

In some cases, there may be a final interview with senior management or a cross-functional team. This stage is designed to assess your alignment with ServiceNow's values and your ability to collaborate with different stakeholders. You may be asked to discuss your vision for data engineering within the company and how you would contribute to its strategic goals.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and your ability to work within a team-oriented environment.

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

Understand the Interview Process

Be prepared for a potentially lengthy and multi-stage interview process. Candidates have reported multiple rounds of interviews, often with tight scheduling and limited time to engage with interviewers. Familiarize yourself with the typical structure of the interviews, which may include technical assessments and discussions with various stakeholders. This will help you manage your time effectively and ensure you can convey your thoughts clearly within the allotted time.

Prepare for Technical Questions

Given the emphasis on SQL and data modeling in the role, brush up on your technical skills, particularly in SQL queries, data architecture, and data pipeline design. Practice articulating your thought process while solving problems, as some candidates have noted that they were not allowed to write out queries during the interview. Being able to explain your approach verbally is crucial, so consider practicing with a peer or mentor.

Showcase Your Experience with Data Systems

Highlight your experience with data systems, particularly in AI and machine learning, as this is a key requirement for the role. Be ready to discuss specific projects where you have successfully implemented data solutions, focusing on how you translated business requirements into actionable data models. Use concrete examples to demonstrate your ability to lead cross-functional teams and manage data strategies effectively.

Engage with the Interviewers

Candidates have noted mixed experiences with interviewers at ServiceNow. Some found them to be supportive and engaging, while others felt disconnected. Approach each interviewer with enthusiasm and curiosity. Ask insightful questions about their experiences and the challenges they face in their roles. This not only shows your interest in the position but also helps build rapport, which can be beneficial in a potentially high-stakes environment.

Emphasize Collaboration and Communication Skills

Given the collaborative nature of the role, be prepared to discuss how you have worked with various stakeholders in the past. Highlight your ability to establish strong working relationships and communicate complex data concepts in an understandable way. This is particularly important as you will need to engage with both technical and non-technical team members.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities, adaptability, and teamwork. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you provide clear and concise examples from your past experiences. This will help you convey your qualifications effectively and demonstrate your fit for the company culture.

Stay Positive and Resilient

Some candidates have expressed frustration with the interview process, including delays and unclear communication. Regardless of your experience, maintain a positive attitude throughout the interview. Show resilience and adaptability, as these traits align well with ServiceNow's culture of innovation and transformation. Your ability to stay composed and focused will leave a lasting impression.

By following these tips, you can position yourself as a strong candidate for the Data Engineer role at ServiceNow. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at ServiceNow. The interview process will likely focus on your technical skills, problem-solving abilities, and experience with data architecture and management. Be prepared to discuss your past projects, the tools you've used, and how you've approached data challenges in your previous roles.

Technical Skills

1. Can you explain the differences between a star schema and a snowflake schema?

Understanding data modeling is crucial for a Data Engineer. This question assesses your knowledge of database design and optimization.

How to Answer

Discuss the structural differences between the two schemas, including their advantages and disadvantages in terms of query performance and data integrity.

Example

“A star schema has a central fact table connected to multiple dimension tables, which simplifies queries and improves performance. In contrast, a snowflake schema normalizes the dimension tables into multiple related tables, which can save space but may complicate queries. I prefer using a star schema for reporting purposes due to its simplicity and speed.”

2. Describe your experience with ETL processes. What tools have you used?

This question evaluates your hands-on experience with data extraction, transformation, and loading.

How to Answer

Mention specific ETL tools you have used, the types of data you worked with, and any challenges you faced during the process.

Example

“I have extensive experience with ETL processes using tools like Apache NiFi and Talend. In my last project, I designed an ETL pipeline that integrated data from various sources, including APIs and databases, ensuring data quality and consistency throughout the process.”

3. How do you ensure data quality and integrity in your data pipelines?

Data quality is paramount in data engineering. This question assesses your approach to maintaining high standards in data management.

How to Answer

Discuss the methods you use to validate data, handle errors, and ensure that data remains accurate and reliable.

Example

“I implement data validation checks at various stages of the pipeline, such as schema validation and data type checks. Additionally, I use logging and monitoring tools to track data quality metrics and quickly identify any anomalies that may arise.”

4. What is your experience with cloud data storage solutions?

As ServiceNow emphasizes cloud technology, this question gauges your familiarity with cloud platforms.

How to Answer

Mention specific cloud services you have worked with, such as AWS, Azure, or Google Cloud, and how you utilized them for data storage and processing.

Example

“I have worked extensively with AWS, particularly with S3 for data storage and Redshift for data warehousing. I appreciate the scalability and flexibility these services offer, allowing me to handle large datasets efficiently.”

5. Can you explain the concept of data partitioning and its benefits?

This question tests your understanding of data management techniques that enhance performance.

How to Answer

Define data partitioning and explain how it can improve query performance and manageability.

Example

“Data partitioning involves dividing a large dataset into smaller, more manageable pieces, which can significantly improve query performance by allowing the database to scan only relevant partitions. For instance, I partitioned a sales dataset by date, which reduced query times for time-based analyses.”

SQL and Database Management

1. How would you write a SQL query to find the top N records in a dataset?

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

How to Answer

Explain the SQL syntax you would use and any considerations for performance.

Example

“I would use the SELECT statement with the ORDER BY clause to sort the data and the LIMIT clause to restrict the number of records returned. For example: SELECT * FROM sales ORDER BY revenue DESC LIMIT 10; This approach ensures I retrieve the top 10 records based on revenue.”

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

This question evaluates your advanced SQL knowledge and understanding of analytical functions.

How to Answer

Define window functions and provide examples of scenarios where they are beneficial.

Example

“Window functions perform calculations across a set of table rows related to the current row. I often use them for running totals or moving averages, as they allow me to analyze data without collapsing the result set. For instance, using SUM(sales) OVER (PARTITION BY region ORDER BY date) gives me a running total of sales by region.”

3. Can you explain the concept of normalization and denormalization?

This question tests your understanding of database design principles.

How to Answer

Discuss the goals of normalization and denormalization, along with their respective advantages and disadvantages.

Example

“Normalization reduces data redundancy and improves data integrity by organizing data into related tables. However, it can lead to complex queries. Denormalization, on the other hand, combines tables to improve read performance at the cost of increased redundancy. I typically normalize data for transactional systems and denormalize for analytical systems.”

4. Describe a challenging SQL query you had to write. What made it difficult?

This question assesses your problem-solving skills and ability to handle complex data scenarios.

How to Answer

Share a specific example, detailing the complexity of the query and how you approached the challenge.

Example

“I once had to write a query to analyze customer purchase patterns over time, which involved multiple joins and subqueries. The challenge was ensuring performance while retrieving accurate data. I optimized the query by indexing key columns and breaking it down into smaller parts, which significantly improved execution time.”

5. How do you handle performance tuning in SQL queries?

This question evaluates your ability to optimize database performance.

How to Answer

Discuss the techniques you use to identify and resolve performance issues in SQL queries.

Example

“I start by analyzing the execution plan to identify bottlenecks. I then look for opportunities to add indexes, rewrite queries for efficiency, and reduce the number of joins where possible. For instance, I once improved a slow-running report by replacing a subquery with a join, which reduced the execution time by over 50%.”