ServiceTitan Data Engineer Interview Questions + Guide in 2025

ServiceTitan is a leading SaaS provider that empowers field service businesses to optimize their operations, increase customer retention, and maximize revenue through a user-friendly cloud-based platform.

As a Data Engineer at ServiceTitan, you will play a critical role in managing and delivering data solutions that enhance the company's data-driven products. You will be responsible for designing and implementing scalable data architectures, creating efficient data pipelines, and ensuring seamless data movement across various systems. Your expertise will support the development of multi-tenant data infrastructure while adhering to best practices in data governance, compliance, and security.

Key responsibilities include collaborating with cross-functional teams to build robust customer solutions, automating data quality monitoring, and driving end-to-end performance of data platforms. Additionally, you will be expected to champion high-quality code practices, participate in code reviews, and engage in continuous learning to stay updated with the latest data engineering trends and technologies.

A successful candidate will possess strong technical skills, including proficiency in SQL and programming languages such as Python or Scala, along with experience in building Spark applications and working with large-scale distributed storage systems. Excellent communication skills and the ability to work effectively within a collaborative team environment are also essential.

This guide will help you prepare for your job interview by offering insights into the key skills and attributes that ServiceTitan values, as well as providing tailored questions to expect during the interview process.

The interview process for a Data Engineer role at ServiceTitan 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 around 30 minutes. This conversation is typically conducted by a recruiter who will discuss your background, the role, and the company culture. This is an opportunity for you to ask questions about ServiceTitan and gauge if it aligns with your career goals. The recruiter will also evaluate your communication skills and overall fit for the team.

2. Technical Assessment

Following the initial screen, candidates may be required to complete a technical assessment. This could involve a coding test on platforms like HackerRank, focusing on SQL and data manipulation tasks. The assessment is designed to evaluate your problem-solving abilities and familiarity with data engineering concepts. Be prepared for questions that may require you to write SQL queries or solve data-related problems.

3. Hiring Manager Interview

Next, candidates typically have a phone interview with the hiring manager. This conversation may be more technical in nature, focusing on your experience and specific skills relevant to the role. Expect questions that assess your knowledge of data engineering principles, tools, and technologies. The hiring manager may also inquire about your past projects and how you approach problem-solving in a data context.

4. Onsite Interviews

The final stage usually involves onsite interviews, which can consist of multiple rounds with various team members, including senior engineers, managers, and possibly directors. Each interview may last around 45 minutes and cover a mix of technical and behavioral questions. You may be asked to solve coding problems on a whiteboard or through a collaborative coding environment. This is also a chance for you to demonstrate your ability to communicate complex ideas clearly and effectively.

Throughout the process, candidates are encouraged to engage with interviewers, ask questions, and showcase their passion for data engineering and the mission of ServiceTitan.

As you prepare for your interviews, consider the types of questions that may arise in each of these stages.

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

Prepare for a Structured Interview Process

The interview process at ServiceTitan typically involves multiple stages, starting with a recruiter conversation, followed by a technical interview with team members. Familiarize yourself with the structure of the interview and prepare accordingly. Be ready to discuss your experience and how it aligns with the role, as well as to answer technical questions, particularly around SQL and data engineering concepts. Practicing coding problems on platforms like HackerRank or LeetCode can be beneficial, especially for SQL-related questions.

Showcase Your Technical Proficiency

As a Data Engineer, you will be expected to demonstrate a strong command of SQL and data engineering principles. Brush up on your SQL skills, focusing on complex queries, joins, and data manipulation techniques. Be prepared to write code on a whiteboard or in a shared document, as this is a common practice during interviews. Additionally, familiarize yourself with the tools and technologies mentioned in the job description, such as Python, Spark, and distributed storage systems, as these may come up in technical discussions.

Communicate Effectively

Effective communication is key during the interview process. Be clear and concise in your responses, and don’t hesitate to ask for clarification if a question is not clear. Given that some interviewers may have a more abrupt style, maintaining a calm and professional demeanor will help you navigate the conversation smoothly. Additionally, be prepared to discuss how you can contribute to cross-functional teams and support the company’s data-driven goals.

Understand the Company Culture

ServiceTitan values individuality and a collaborative work environment. During your interview, express your enthusiasm for being part of a team that embraces diverse perspectives. Share examples of how you have worked collaboratively in the past and how you can contribute to a positive team culture. This will demonstrate that you not only have the technical skills but also align with the company’s values.

Be Ready for Behavioral Questions

Expect to encounter behavioral questions that assess your problem-solving abilities and how you handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Prepare specific examples from your past experiences that highlight your technical skills, teamwork, and adaptability. This will help you convey your qualifications effectively and show that you are a well-rounded candidate.

Follow Up with Questions

At the end of your interview, take the opportunity to ask insightful questions about the team, projects, and company culture. This not only shows your interest in the role but also helps you gauge if ServiceTitan is the right fit for you. Consider asking about the team’s current challenges, the technologies they are exploring, or how they measure success in the data engineering department.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at ServiceTitan. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at ServiceTitan. The interview process will likely focus on your technical skills, problem-solving abilities, and understanding of data engineering principles. Be prepared to discuss your experience with data pipelines, SQL, and data governance, as well as your ability to work collaboratively with cross-functional teams.

SQL and Database Management

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

Understanding SQL joins is crucial for data manipulation and retrieval.

How to Answer

Clearly define both types of joins and provide examples of when each would be used in a query.

Example

“A LEFT JOIN returns all records from the left table and the matched records from the right table, while an INNER JOIN returns only the records that have matching values in both tables. For instance, if I have a table of customers and a table of orders, a LEFT JOIN would show all customers, including those who haven’t placed any orders, whereas an INNER JOIN would only show customers who have made purchases.”

2. How would you write a SQL query to find duplicate records in a table?

This question tests your ability to identify data quality issues.

How to Answer

Discuss the use of GROUP BY and HAVING clauses to find duplicates.

Example

“To find duplicates, I would use a query like: SELECT column_name, COUNT(*) FROM table_name GROUP BY column_name HAVING COUNT(*) > 1; This will return all values in the specified column that appear more than once.”

3. Describe a situation where you had to optimize a slow-running SQL query. What steps did you take?

Performance tuning is a key aspect of data engineering.

How to Answer

Explain your approach to identifying performance bottlenecks and the techniques you used to optimize the query.

Example

“I once encountered a slow query that was causing delays in our reporting system. I analyzed the execution plan and found that it was performing a full table scan. I added appropriate indexes and restructured the query to use JOINs instead of subqueries, which improved the performance significantly.”

4. What is a nested query, and when would you use one?

Nested queries are often used for complex data retrieval.

How to Answer

Define nested queries and provide a scenario where they are beneficial.

Example

“A nested query, or subquery, is a query within another SQL query. I would use a nested query when I need to filter results based on the results of another query. For example, to find customers who have placed orders above a certain amount, I would first query the orders table to get the relevant order IDs and then use that result to filter the customers.”

5. How do you ensure data integrity when working with multiple data sources?

Data integrity is critical in data engineering.

How to Answer

Discuss your strategies for maintaining data quality and consistency across different systems.

Example

“I ensure data integrity by implementing validation checks during the ETL process, using data profiling tools to assess data quality, and establishing clear data governance policies. Additionally, I regularly audit data flows to identify and rectify any discrepancies.”

Data Engineering Principles

1. What is ETL, and can you describe the process?

Understanding ETL is fundamental for a Data Engineer.

How to Answer

Explain the components of ETL and their significance in data processing.

Example

“ETL stands for Extract, Transform, Load. The process begins with extracting data from various sources, transforming it into a suitable format for analysis, and finally loading it into a data warehouse. This process is essential for consolidating data from disparate systems into a single source of truth.”

2. Can you explain what a data pipeline is and its importance?

Data pipelines are central to data engineering workflows.

How to Answer

Define data pipelines and discuss their role in data processing.

Example

“A data pipeline is a series of data processing steps that involve the collection, transformation, and storage of data. They are crucial for automating data workflows, ensuring timely data availability for analysis, and maintaining data quality throughout the process.”

3. Describe your experience with distributed storage systems. Which ones have you worked with?

Familiarity with distributed systems is often required for data engineering roles.

How to Answer

Mention specific technologies you’ve used and your experience with them.

Example

“I have worked extensively with distributed storage systems like Hadoop and Cassandra. In my previous role, I used Hadoop for batch processing large datasets and Cassandra for real-time data storage, which allowed us to handle high-velocity data efficiently.”

4. How do you approach data governance and compliance in your projects?

Data governance is essential for maintaining data quality and security.

How to Answer

Discuss your understanding of data governance principles and how you implement them.

Example

“I approach data governance by establishing clear policies for data access, usage, and quality. I ensure compliance with regulations like GDPR by implementing data anonymization techniques and maintaining thorough documentation of data lineage and access controls.”

5. What tools and technologies do you prefer for building data pipelines?

Your choice of tools can reflect your technical expertise.

How to Answer

Mention specific tools you have experience with and why you prefer them.

Example

“I prefer using Apache Airflow for orchestrating data pipelines due to its flexibility and ease of use. For data transformation, I often use Apache Spark because of its ability to handle large-scale data processing efficiently. Additionally, I leverage tools like dbt for data modeling and transformation.”