What is a Data Engineer at DoubleVerify?
As a Data Engineer at DoubleVerify, you will play a pivotal role in building and maintaining the data infrastructure that powers our products and services. This position is critical for ensuring that our data is reliable, accessible, and can be leveraged strategically across various teams. You will contribute to vital products that enhance digital advertising transparency and effectiveness, impacting users by providing them with accurate insights and analytics.
In your role, you will work with complex data sets, employing advanced technologies and methodologies to ensure data quality and integrity. Your contributions will directly influence how our clients make informed decisions based on the data we provide, making this position not only technically challenging but also strategically significant. The environment is dynamic, and you will have the opportunity to collaborate with cross-functional teams, driving innovation and efficiency in our data processes.
Common Interview Questions
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Curated questions for DoubleVerify from real interviews. Click any question to practice and review the answer.
Design a batch ETL pipeline that detects, imputes, and monitors missing values before loading analytics tables with daily SLA compliance.
Design a batch data pipeline with quality gates, quarantine handling, and monitored reprocessing for 120M finance records per day.
Design Terraform-based infrastructure as code for AWS data pipelines with reusable modules, secure state management, CI/CD, and drift control.
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Sign up freeAlready have an account? Sign inGetting Ready for Your Interviews
Preparing for your interviews requires a strategic approach. You should focus on demonstrating your technical expertise and your ability to fit within the DoubleVerify culture.
Role-related knowledge – You need to exhibit a strong understanding of data engineering concepts, tools, and technologies relevant to the role. Interviewers will look for your ability to discuss and apply these concepts effectively.
Problem-solving ability – Your approach to complex problems is crucial. Be prepared to walk through your thought processes and demonstrate how you break down challenges into manageable parts.
Leadership – While you may not be in a formal leadership role, your ability to influence and collaborate with others will be evaluated. Show how you communicate effectively and help drive team initiatives forward.
Culture fit / values – DoubleVerify values collaboration, innovation, and integrity. You should be ready to discuss how your personal values align with the company’s mission and culture.
Interview Process Overview
The interview process at DoubleVerify is structured yet adaptable, beginning with a phone screening and progressing to more in-depth technical and behavioral interviews. You can expect a balanced focus on both technical skills and cultural fit, with a notable emphasis on collaboration and problem-solving abilities.
Candidates typically start with an HR call, followed by one or more technical interviews where you will engage with team leads and senior engineers. The process may culminate in discussions with higher management, such as the VP of Engineering, to assess both your technical acumen and your fit within the team.
The visual timeline illustrates the different stages of the interview process, from initial screening to final interviews. Use this to plan your preparation effectively, ensuring you allocate time for each phase of the process. Remember that while the structure is consistent, the specifics may vary by team or location.
Deep Dive into Evaluation Areas
In this section, we will explore the major evaluation areas that interviewers focus on during the interview process. Understanding these areas can help you prepare more effectively.
Role-related Knowledge
This area assesses your technical expertise in data engineering. Interviewers will evaluate your familiarity with relevant tools, technologies, and methodologies.
- Data modeling – Understand different data models and their use cases.
- Data warehousing – Be prepared to discuss concepts like star and snowflake schemas.
- ETL processes – Know the intricacies of ETL tools and workflows.
- Big data technologies – Familiarity with tools like Hadoop and Spark can set you apart.
Example questions:
- Describe the differences between OLTP and OLAP systems.
- How would you choose an ETL tool for a specific project?
Problem-Solving Ability
Your analytical thinking and problem-solving skills are crucial for success in this role. Interviewers will present you with scenarios to evaluate your approach to tackling complex challenges.
- Analytical frameworks – Discuss your methodologies for analyzing data issues.
- Debugging strategies – Be ready to describe how you troubleshoot data pipelines.
Example questions:
- Given a dataset with missing values, how would you handle it?
- How do you prioritize data integrity issues?
Leadership and Collaboration
This area focuses on how you interact with others and your ability to contribute to a team dynamic. Strong candidates demonstrate effective communication and collaboration skills.
- Influencing without authority – Explain how you drive initiatives in a team environment.
- Cross-functional collaboration – Share experiences working with product or engineering teams.
Example questions:
- How would you handle disagreements with a product manager regarding data requirements?
- Describe how you’ve helped mentor junior engineers.
Advanced Concepts
Interviewers may also explore advanced topics that distinguish high-performing candidates. Familiarity with cutting-edge technologies and methodologies can be beneficial.
- Machine learning data pipelines – Understanding how to integrate ML models into data workflows.
- Data governance – Be prepared to discuss frameworks for data compliance and ethics.
Example questions:
- How would you implement a data governance framework in a large organization?
- Discuss your experience with machine learning model deployment.
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