As a Data Engineer at Reddit, you will design, build, and scale the data systems that power one of the most visited websites in the world. With hundreds of millions of active users generating massive volumes of posts, comments, votes, and clicks every single day, data is the lifeblood of Reddit. The data infrastructure you build directly influences critical product decisions, ad targeting algorithms, search functionality, and community safety initiatives.

Your work will involve handling data at petabyte scale, translating raw user interactions into structured, high-performance data lakes and warehouses. You will be responsible for ensuring that product managers, data scientists, and machine learning engineers have access to reliable, real-time, and batch-processed data. This role requires a unique blend of software engineering discipline and deep data domain expertise to keep Reddit's data pipelines running efficiently around the clock.

Joining Reddit as a Data Engineer means solving complex distributed systems challenges that few other companies face. You will work on optimizing pipeline performance, reducing query latency, and managing the cost of massive cloud-based data warehouses. It is a highly collaborative and impactful role where your technical decisions will shape the user experience for millions of communities worldwide.

To help you prepare effectively, we have compiled representative questions based on real interview experiences at Reddit. These questions are grouped into core categories to help you identify patterns and focus your preparation on the areas that matter most.

SQL & Data Modeling

These questions evaluate your ability to write clean, efficient queries and design scalable data warehouse schemas.

• Given a table of user upvotes and downvotes, write a query to find the top trending subreddits based on activity velocity over the last 24 hours.
• How would you design a star schema for tracking user ad impressions and clicks across different platforms?

Preparing for an interview at Reddit requires a balanced approach. You must demonstrate strong software engineering fundamentals alongside specialized data engineering expertise. Reviewers look for candidates who can write production-grade code, design highly scalable systems, and collaborate effectively across teams.

SQL Mastery – You must be highly proficient in SQL. Interviewers will evaluate your ability to write complex queries, use window functions, optimize joins, and understand underlying execution plans. Brush up on data warehousing concepts, including normalization, denormalization, and partition strategies.

Algorithmic Problem-Solving – You need to write clean, runnable code during live coding sessions. Focus on mastering core data structures such as hash maps, trees, queues, and stacks. Be prepared to explain the time and space complexity of your solutions.

System Design & Scalability – You should be ready to architect large-scale data systems. You will be evaluated on your understanding of modern data technologies (e.g., Spark, Kafka, Airflow, Snowflake), batch versus streaming paradigms, and how to design fault-tolerant, cost-effective pipelines.

Collaboration & Ambiguity – You must show that you can thrive in a fast-paced environment. Reddit values engineers who take ownership, communicate clearly with non-technical stakeholders, and can navigate ambiguous problem spaces with a user-first mindset.

The interview process for a Data Engineer at Reddit is rigorous and designed to evaluate both your technical depth and cultural alignment. Candidates typically report a structured progression that begins with initial screening and culminates in a comprehensive virtual onsite loop.

The journey starts with a friendly conversation with a recruiter to discuss your background, your interest in Reddit, and basic role alignment. This is followed by a technical screen, which frequently focuses on core SQL concepts and live coding via a collaborative platform like Coderpad. If you pass this stage, you will move to the virtual onsite loop, which consists of multiple rounds covering system design, advanced coding, data warehousing concepts, and behavioral evaluations.

The visual timeline above outlines the typical stages of the Reddit hiring journey. Candidates should use this roadmap to pace their preparation, ensuring they master SQL and basic algorithms before the technical screen, and save deep-dive system design practice for the onsite loop. Note that while the flow is standardized, the specific focus of technical rounds can occasionally shift depending on whether the team leans more toward data warehousing or infrastructure engineering.

To succeed at Reddit, you must perform consistently across several core technical domains. Below is a detailed breakdown of what to expect and how to demonstrate mastery in each key area.

SQL & Data Warehousing

SQL is a foundational skill for any Data Engineer at Reddit. You will face dedicated rounds testing your ability to manipulate large datasets and design clean, efficient schemas that support analytical use cases.

Be ready to go over:

• Analytical Window Functions – Mastering functions like ROW_NUMBER(), RANK(), LEAD(), LAG(), and running totals.
• Data Modeling Paradigms – Designing star schemas, snowflake schemas, and understanding when to use slowly changing dimensions (SCDs).
• Query Optimization – Identifying performance bottlenecks, understanding indexing, partitioning, and clustering in modern cloud warehouses.
• Advanced concepts (less common) – Incremental table updates, materialization strategies, and handling complex nested JSON data in SQL.

Example questions or scenarios:

• "Design a schema to track user interactions with subreddits, including visits, posts, and comments, optimizing for daily active user (DAU) reporting."
• "Write a query to find the top 3 most popular posts in each subreddit for every hour of the day."

Coding & Systems Programming

Reddit expects its data engineers to write high-quality, maintainable code. You will participate in live coding sessions where you must solve algorithmic challenges and design custom data structures.

Be ready to go over:

• Data Structure Design – Creating custom classes and objects that meet specific time and space complexity requirements.
• File and Stream Processing – Writing memory-efficient code to parse, filter, and aggregate large text files or streaming payloads.
• Error Handling & Testing writing robust code that handles edge cases, null values, and malformed inputs gracefully.
• Advanced concepts (less common) – Multi-threading, concurrency, and custom generator patterns in Python.

Example questions or scenarios:

• "Using Coderpad, design a class that acts as a custom queue with a built-in deduplication mechanism."
• "Implement an algorithm to merge multiple sorted streams of log data into a single sorted output stream."

Data Systems Design

This round tests your ability to architect end-to-end data systems at scale. You will be given a high-level prompt and asked to design a solution, making trade-offs between latency, cost, and complexity.

Be ready to go over:

• Batch vs. Streaming – Choosing between technologies like Spark and Flink based on the real-time requirements of the business.
• Message Broking & Ingestion – Designing reliable ingestion layers using Kafka or Kinesis, including partition strategies.
• Orchestration & Lineage – Structuring complex DAGs in Airflow and ensuring data quality checks are built-in.
• Advanced concepts (less common) – Backpressure management, exactly-once processing semantics, and schema evolution.

Example questions or scenarios:

• "Design a real-time system to detect and flag spam comments across all of Reddit within 5 seconds of creation."
• "Architect a pipeline to ingest 10 billion events per day from mobile clients, process them, and load them into a data lake for ad-hoc analysis."