Reddit, Inc. Data Engineer Interview Questions + Guide in 2025

Reddit is a vibrant platform known as a "community of communities," where users engage in authentic discussions on a myriad of topics, fostering shared interests and trust.

As a Data Engineer at Reddit, you will lead the development of robust data infrastructure that empowers various teams to make data-driven decisions. Your primary responsibilities will include building and maintaining scalable ETL systems, creating user-friendly data tools, and ensuring the quality of data pipelines that support both analytics and machine learning initiatives. You will collaborate closely with cross-functional teams, including product, marketing, and engineering, to streamline data processes and drive the adoption of data self-service practices. A successful Data Engineer at Reddit will not only possess strong technical skills in programming languages such as Python and SQL but will also demonstrate a passion for fostering a data-centric culture throughout the organization.

This guide will help you prepare effectively for your interview by providing insights into the key competencies and expectations for the Data Engineer role at Reddit, tailored to align with the company's collaborative and innovative ethos.

The interview process for a Data Engineer role at Reddit is structured to assess both technical skills and cultural fit within the company. It typically consists of several key stages:

1. Initial Recruiter Call

The process begins with a phone call from a recruiter. This conversation is generally informal and serves as an opportunity for the recruiter to gauge your interest in the role and the company. You will discuss your background, experience, and motivations for applying to Reddit. The recruiter may also provide insights into the company culture and the specifics of the Data Engineer position.

2. Technical Screening

Following the initial call, candidates usually undergo a technical screening, which may be conducted via video call. This round typically focuses on your proficiency in SQL and other relevant programming languages such as Python or Scala. Expect to answer questions related to data structures, ETL processes, and possibly solve simple coding challenges. The interviewer will assess your problem-solving skills and your ability to articulate your thought process.

3. Technical Interviews

Candidates who pass the technical screening will move on to one or more technical interviews. These interviews are often conducted by members of the data engineering team and may include a mix of coding exercises, system design questions, and discussions about your previous projects. You may be asked to demonstrate your understanding of data pipelines, data modeling, and data governance practices. Be prepared to discuss your experience with tools like Airflow, Spark, and data visualization platforms.

4. Behavioral Interviews

In addition to technical assessments, candidates will likely participate in behavioral interviews. These interviews focus on your interpersonal skills, teamwork, and how you align with Reddit's values. Expect questions that explore your past experiences working in cross-functional teams, mentoring others, and driving data-driven decision-making within an organization.

5. Final Interview

The final stage may involve a conversation with senior leadership or cross-functional stakeholders. This interview is designed to assess your strategic thinking and ability to communicate complex data concepts to non-technical audiences. You may also discuss your vision for data engineering at Reddit and how you can contribute to building a data-driven culture.

As you prepare for your interviews, it's essential to familiarize yourself with the types of questions that may be asked during each stage.

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

Prepare for Technical Questions

Given the emphasis on SQL and data structures in the interview process, it's crucial to brush up on your SQL skills. Be ready to answer questions that involve writing queries to extract and manipulate data. Practice common SQL problems, especially those that involve joins, aggregations, and subqueries. Additionally, familiarize yourself with Python, as there may be questions related to data manipulation and ETL processes.

Understand the Company Culture

Reddit values a community-driven approach, so demonstrating your ability to collaborate and communicate effectively with cross-functional teams will be key. Be prepared to discuss how you have worked with product, marketing, and engineering teams in the past. Highlight your experience in fostering a data-driven culture and how you can contribute to Reddit's mission of making data accessible across the organization.

Showcase Your Problem-Solving Skills

During the interview, you may encounter scenario-based questions that assess your problem-solving abilities. Approach these questions by clearly outlining your thought process. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey how you tackled challenges in previous roles, particularly in data engineering contexts.

Be Ready for Behavioral Questions

Expect behavioral questions that explore your past experiences and how they align with Reddit's values. Reflect on your previous roles and prepare examples that demonstrate your leadership, mentorship, and ability to drive projects to completion. Given the feedback from candidates, showing enthusiasm and a genuine interest in Reddit's future can set you apart.

Engage with Your Interviewers

Interviews at Reddit have been described as friendly and low-stress. Use this to your advantage by engaging with your interviewers. Ask insightful questions about their experiences at Reddit, the team dynamics, and the challenges they face. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your values.

Follow Up Professionally

After your interview, send a thoughtful follow-up email to express your gratitude for the opportunity to interview. Mention specific topics discussed during the interview to reinforce your interest in the role and the company. This small gesture can leave a lasting impression and demonstrate your professionalism.

By preparing thoroughly and aligning your experiences with Reddit's values and expectations, you can position yourself as a strong candidate for the Data Engineer role. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Reddit. The interview process will likely focus on your technical skills, particularly in data engineering, ETL processes, and your ability to work collaboratively across teams. Be prepared to demonstrate your knowledge of data structures, SQL, and Python, as well as your experience with data pipelines and visualization tools.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it forms the backbone of data management and analytics.

How to Answer

Discuss the steps involved in ETL, emphasizing how each step contributes to data quality and accessibility. Mention any specific tools or frameworks you have used in your ETL processes.

Example

“ETL is essential for transforming raw data into a usable format for analysis. In my previous role, I utilized Apache Airflow for orchestrating ETL workflows, ensuring data was extracted from various sources, transformed to meet business requirements, and loaded into our data warehouse for reporting.”

2. Describe a challenging data pipeline you built. What were the key considerations?

This question assesses your practical experience and problem-solving skills in building data pipelines.

How to Answer

Focus on the challenges you faced, the technologies you used, and how you ensured data integrity and performance.

Example

“I built a data pipeline that ingested data from multiple APIs and processed it in real-time. Key considerations included handling data latency and ensuring fault tolerance. I implemented a retry mechanism and used Kafka for message queuing, which significantly improved the reliability of the pipeline.”

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

Data quality is paramount in data engineering, and interviewers want to know your strategies for maintaining it.

How to Answer

Discuss specific techniques you use for data validation, monitoring, and error handling.

Example

“I implement data validation checks at various stages of the ETL process. For instance, I use schema validation to ensure incoming data matches expected formats and run periodic audits to identify anomalies. Additionally, I set up alerts for any data quality issues that arise during processing.”

4. What experience do you have with data modeling?

Data modeling is a critical skill for a Data Engineer, and this question gauges your understanding of how to structure data effectively.

How to Answer

Explain your approach to data modeling, including any methodologies you prefer and tools you have used.

Example

“I have experience with both star and snowflake schemas for data warehousing. In my last project, I designed a star schema to optimize query performance for our reporting needs, using tools like dbt for transformation and Looker for visualization.”

5. Can you discuss your experience with SQL and any complex queries you’ve written?

SQL proficiency is essential for a Data Engineer, and interviewers will want to see your ability to write complex queries.

How to Answer

Provide examples of complex SQL queries you’ve written, explaining the context and the results.

Example

“I frequently write complex SQL queries to analyze user behavior. For example, I created a query that joined multiple tables to calculate the average session duration per user segment, which helped the marketing team tailor their campaigns effectively.”

Programming and Tools

1. What programming languages are you proficient in, and how have you used them in data engineering?

This question assesses your technical skills and familiarity with programming languages relevant to data engineering.

How to Answer

Mention the languages you are proficient in, particularly Python and any others relevant to the role, and provide examples of how you’ve used them.

Example

“I am proficient in Python and SQL, which I use extensively for data manipulation and ETL processes. For instance, I developed a Python script that automated data cleaning tasks, significantly reducing the time spent on manual data preparation.”

2. Describe your experience with data visualization tools. Which ones do you prefer and why?

Data visualization is an important aspect of data engineering, and this question evaluates your experience with visualization tools.

How to Answer

Discuss the tools you have used, your preferred ones, and the reasons for your preferences.

Example

“I have experience with Tableau and Looker for data visualization. I prefer Looker for its integration with our data warehouse and its ability to create dynamic dashboards that allow stakeholders to explore data interactively.”

3. How do you approach debugging a data pipeline?

Debugging is a critical skill for a Data Engineer, and interviewers want to know your systematic approach to troubleshooting.

How to Answer

Explain your process for identifying and resolving issues in data pipelines.

Example

“When debugging a data pipeline, I start by checking the logs for any error messages. I then isolate the problematic component, whether it’s an ETL job or a data source, and run tests to identify the root cause. I also ensure to document the issue and the resolution for future reference.”

4. Can you explain the differences between relational and non-relational databases?

Understanding database types is essential for a Data Engineer, and this question tests your knowledge in this area.

How to Answer

Discuss the characteristics of both types of databases and when to use each.

Example

“Relational databases, like PostgreSQL, are structured and use SQL for querying, making them ideal for transactional data. Non-relational databases, like MongoDB, are more flexible and can handle unstructured data, which is useful for applications requiring scalability and speed.”

5. What is your experience with cloud platforms for data engineering?

This question assesses your familiarity with cloud technologies, which are increasingly important in data engineering.

How to Answer

Mention any cloud platforms you have worked with and how you utilized them in your projects.

Example

“I have worked extensively with AWS, particularly with services like S3 for data storage and Redshift for data warehousing. I also used AWS Lambda for serverless data processing, which allowed us to scale our data ingestion processes efficiently.”