Chartboost Data Engineer Interview Questions + Guide in 2025

Chartboost is a leading in-app monetization and programmatic advertising platform, reaching over 700 million monthly active users and processing an astounding 2.7 trillion advertising auctions each month.

As a Data Engineer at Chartboost, you will play a pivotal role in building and maintaining robust data platforms that facilitate data analysis, user modeling, and scalable training systems. Your key responsibilities will include developing high-quality data pipelines, designing and implementing both real-time and batch processing systems, and collaborating with product and business teams to deliver impactful features. To excel in this role, proficiency in SQL and programming languages such as Python, Java, or Scala is essential, as well as experience with cloud environments like GCP or AWS. The ideal candidate will possess strong debugging skills, a passion for automation, and the ability to work autonomously within a collaborative, agile team environment. Chartboost values innovation and continuous improvement, making it vital for you to be comfortable dealing with large volumes of data while maintaining high standards of quality and reliability.

This guide will help you prepare effectively for your interview by offering insights into the skills and experiences that Chartboost prioritizes, allowing you to present yourself as a well-qualified candidate ready to contribute to their data engineering efforts.

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

1. Initial Screening

The process begins with an initial screening call, usually conducted by a recruiter. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Chartboost. The recruiter will also provide insights into the company culture and the specifics of the Data Engineer role.

2. Hiring Manager Interview

Following the initial screening, candidates typically have a one-on-one interview with the hiring manager. This session delves deeper into your technical expertise, particularly in SQL and programming languages such as Python, Java, or Scala. The hiring manager will assess your understanding of data engineering principles and your ability to contribute to the team.

3. Technical Assessment

Candidates then undergo a technical assessment, which may include a coding interview or a take-home assignment. This stage is crucial for evaluating your problem-solving skills and familiarity with data processing tools and frameworks. Expect to work on tasks that involve SQL queries, data pipeline design, and possibly some coding challenges related to data manipulation.

4. Onsite Interviews

The final stage usually consists of multiple onsite interviews, which may be conducted virtually. This round typically includes several interviews with different team members, including engineers and possibly upper management. These interviews will cover a range of topics, including your technical skills, past project experiences, and how you approach problem-solving in a collaborative environment. Behavioral questions may also be included to gauge your fit within the team culture.

Throughout the process, candidates are encouraged to demonstrate their technical knowledge, analytical thinking, and ability to work in a fast-paced, agile environment.

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

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

Understand the Interview Structure

The interview process at Chartboost typically involves multiple stages, including an initial screening with a recruiter, a technical interview, and a series of panel interviews. Familiarize yourself with this structure so you can prepare accordingly. Knowing what to expect will help you manage your time and energy effectively throughout the process.

Showcase Your Technical Skills

As a Data Engineer, you will be expected to demonstrate proficiency in SQL, Python, and possibly Java or Scala. Brush up on your coding skills, particularly in SQL, as it is a significant part of the role. Practice solving problems on platforms like LeetCode, focusing on easy to medium-level challenges. Be prepared to discuss your experience with data pipelines and any relevant projects you've worked on, especially those involving large datasets.

Prepare for Behavioral Questions

Chartboost values collaboration and problem-solving skills. Expect behavioral questions that assess how you work with others and handle challenges. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Think of specific examples from your past experiences that highlight your ability to work in a team, prioritize tasks, and solve complex problems.

Emphasize Your Experience with Data Technologies

Given the company's focus on building and maintaining a robust data platform, be ready to discuss your experience with data technologies such as Spark, Kafka, and cloud environments like GCP or AWS. Highlight any projects where you designed or implemented data processing pipelines, and be prepared to explain your approach to optimizing performance and scaling systems.

Engage with the Interviewers

The interviewers at Chartboost are described as friendly and supportive. Use this to your advantage by engaging them in conversation. Ask insightful questions about the team, the technologies they use, and the challenges they face. This not only shows your interest in the role but also helps you assess if the company culture aligns with your values.

Be Authentic and Reflective

Candidates have noted that the interview process can feel somewhat unstructured. Embrace this by being authentic in your responses. Reflect on your experiences and how they relate to the role. If you encounter questions that seem vague or off-putting, don’t hesitate to ask for clarification or to share your thought process. This demonstrates your critical thinking skills and willingness to engage in dialogue.

Follow Up Professionally

After your interviews, send a thank-you email to your interviewers expressing your appreciation for the opportunity to speak with them. This is a chance to reiterate your interest in the role and to mention any points from the conversation that particularly resonated with you. A thoughtful follow-up can leave a lasting impression and reinforce your enthusiasm for the position.

By preparing thoroughly and approaching the interview with confidence and authenticity, you can position yourself as a strong candidate for the Data Engineer role at Chartboost. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Chartboost. The interview process will likely assess your technical skills, problem-solving abilities, and your experience with data engineering concepts. Be prepared to discuss your past projects, your approach to data pipelines, and how you handle challenges in a collaborative environment.

Technical Skills

1. Can you explain the process of building a data pipeline from scratch?

This question assesses your understanding of data pipeline architecture and your practical experience in building them.

How to Answer

Outline the steps involved in designing, implementing, and deploying a data pipeline. Discuss the tools and technologies you would use, and emphasize your experience with data processing frameworks.

Example

“To build a data pipeline from scratch, I would start by identifying the data sources and the required transformations. I would then choose a suitable framework, such as Apache Airflow for orchestration, and use tools like Spark for processing. After implementing the pipeline, I would set up monitoring to ensure data quality and performance.”

2. How do you optimize SQL queries for better performance?

This question evaluates your SQL skills and your ability to improve data retrieval efficiency.

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans. Provide examples from your experience where you successfully optimized queries.

Example

“I optimize SQL queries by first analyzing the execution plan to identify bottlenecks. I often implement indexing on frequently queried columns and rewrite complex joins into simpler subqueries. For instance, in a previous project, I reduced query execution time by 50% by restructuring the joins and adding appropriate indexes.”

3. Describe your experience with cloud platforms, specifically GCP or AWS.

This question gauges your familiarity with cloud environments, which is crucial for the role.

How to Answer

Share specific projects where you utilized cloud services, focusing on the tools and services you used and the outcomes achieved.

Example

“I have extensive experience with GCP, particularly with BigQuery and Dataflow. In my last role, I migrated our data processing workflows to GCP, which improved our data processing speed by 30% and allowed for better scalability as our data volume grew.”

4. What strategies do you use for debugging data pipelines?

This question tests your problem-solving skills and your approach to maintaining data integrity.

How to Answer

Discuss your systematic approach to identifying and resolving issues in data pipelines, including the tools you use for monitoring and debugging.

Example

“When debugging data pipelines, I start by checking the logs for errors and using monitoring tools like Grafana to visualize data flow. I also implement alerting mechanisms to catch issues early. For example, I once identified a data loss issue by setting up alerts for unexpected drops in data volume, which led to a quick resolution.”

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

This question assesses your understanding of data governance and quality assurance practices.

How to Answer

Explain the methods you use to validate and clean data, as well as how you implement checks throughout the data pipeline.

Example

“I ensure data quality by implementing validation checks at various stages of the pipeline. I use tools like Great Expectations to define expectations for data quality and automate testing. Additionally, I conduct regular audits to identify and rectify any discrepancies in the data.”

Problem-Solving and Collaboration

1. Tell me about a time you solved a difficult problem with a simple solution.

This question evaluates your problem-solving skills and your ability to think critically.

How to Answer

Provide a specific example that highlights your analytical skills and your ability to simplify complex issues.

Example

“In a previous project, we faced a significant delay in data processing due to a complex transformation step. I proposed simplifying the transformation logic, which not only reduced processing time but also made the pipeline easier to maintain. This change improved our overall efficiency and allowed us to meet our deadlines.”

2. How do you prioritize tasks when working on multiple projects?

This question assesses your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use to manage your workload effectively.

Example

“I prioritize tasks by assessing their impact and urgency. I often use the Eisenhower Matrix to categorize tasks and focus on high-impact activities first. For instance, during a busy period, I prioritized a critical data migration project over less urgent tasks, ensuring we met our deadlines without compromising quality.”

3. How do you handle feedback from team members or stakeholders?

This question evaluates your interpersonal skills and your ability to work collaboratively.

How to Answer

Share your approach to receiving and implementing feedback, emphasizing your openness to constructive criticism.

Example

“I view feedback as an opportunity for growth. When I receive feedback, I take the time to understand the perspective of the person providing it and assess how I can improve. For example, after receiving feedback on a data visualization I created, I made adjustments based on the team’s input, which ultimately led to a more effective presentation of our findings.”

4. Describe a situation where you had to support another team with their data needs.

This question assesses your collaboration skills and your ability to work cross-functionally.

How to Answer

Provide an example that demonstrates your willingness to assist others and your ability to understand their data requirements.

Example

“I once collaborated with the marketing team to analyze user engagement data. They needed insights to optimize their campaigns, so I created a custom dashboard that visualized key metrics. This not only helped them make data-driven decisions but also strengthened our inter-team relationship.”

5. How do you stay updated with the latest trends and technologies in data engineering?

This question evaluates your commitment to continuous learning and professional development.

How to Answer

Discuss the resources you use to stay informed about industry trends, such as blogs, courses, or conferences.

Example

“I stay updated by following industry blogs like Towards Data Science and attending webinars and conferences. I also participate in online courses to learn new technologies, such as the latest features in cloud platforms. This continuous learning helps me bring innovative solutions to my projects.”