1. What is a Data Engineer at Discord?

At Discord, Data Engineers are the architects behind the infrastructure that supports over 200 million monthly active users. You are not simply moving data from point A to point B; you are building the backbone of a platform where 1.5 billion hours of gameplay and conversation happen every month. Whether you are working on the Data Platform team or focusing on Advertising Products, your work directly impacts how we scale, how we understand user behavior, and how we monetize in a way that respects our community.

This role requires navigating massive scale—processing petabytes of data—while ensuring reliability and speed. You will drive the technical vision for data infrastructure, designing sophisticated pipelines (using tools like Airflow, Dagster, and BigQuery) and building analytical tools that empower Data Scientists, ML Engineers, and Product Managers. You will be solving complex distributed systems problems, often involving real-time data ingestion and high-throughput processing, all while maintaining the playful, inclusive culture that defines our brand.

2. Getting Ready for Your Interviews

Preparation for Discord is about demonstrating technical depth alongside a collaborative spirit. We look for engineers who can write high-quality production code and architect systems that survive the "Discord scale."

Key Evaluation Criteria

Engineering Rigor & Coding – We treat data engineering as software engineering. You must demonstrate proficiency in Python and SQL (and potentially Rust or Java for platform roles). We evaluate your ability to write clean, performant, and maintainable code, not just scripts that "get the job done."

System Design & Architecture – You will be assessed on your ability to design scalable data systems. We look for candidates who understand the trade-offs between batch and streaming, how to handle data quality at scale (billions of rows), and how to architect for observability and fault tolerance in a cloud environment (GCP).

Cross-Functional Collaboration – Discord is a highly collaborative environment. You will be evaluated on how you partner with Data Scientists and Product Managers. We look for technical leaders who can translate vague business requirements into concrete technical specifications and mentor junior engineers.

Culture & Values – We value curiosity, empathy, and a passion for our mission. We want to know how you handle ambiguity, how you resolve technical disagreements, and why you want to work specifically in the gaming and communications space.

3. Interview Process Overview

The interview process at Discord is rigorous and designed to test both your raw technical ability and your fit within our engineering culture. It typically begins with a recruiter screen to align on your background and interests, followed by a technical screen (usually coding-focused) with an engineer.

If you pass the screen, you will move to the onsite loop (virtually). This loop is comprehensive, consisting of multiple rounds covering coding algorithms, SQL/data modeling, system design, and behavioral/experience questions. A distinct feature of our process is the emphasis on level calibration. It is not uncommon for candidates to apply for a Senior (L3) role and, based on the depth of their system design or coding performance, receive an offer for an L2 role. This means consistency across all rounds is vital to securing the level you are targeting.

This timeline illustrates the typical flow from application to offer. Note that the technical rounds are intense; manage your energy to maintain focus during the back-to-back sessions in the onsite loop. Be prepared for a mix of practical coding and high-level architectural discussions.

4. Deep Dive into Evaluation Areas

To succeed, you must demonstrate mastery in the following core areas. We tailor these sessions to real-world problems you would face at Discord.

Coding & Algorithms

This round focuses on your ability to write syntactically correct and logically sound code. Unlike pure software engineering roles that might focus heavily on dynamic programming, Data Engineering coding rounds often lean toward practical data manipulation.

Be ready to go over:

• Data Structures – Using HashMaps, Arrays, and Sets to organize data efficiently.
• String & Array Manipulation – Parsing logs or cleaning unstructured data.
• SQL Fluency – Writing complex queries involving window functions, self-joins, and aggregations without syntax errors.
• Advanced concepts – Optimization of algorithms for time and space complexity (Big O notation).

Example questions or scenarios:

• "Write a function to parse a messy log file and aggregate user session times."
• "Given a stream of gaming events, identify the top 10 most active users in real-time."
• "Write a SQL query to calculate the retention rate of users over a rolling 30-day window."

Data System Design

This is often the most critical round for Senior and Staff roles. You will be asked to architect a solution for a vague problem statement. We are looking for your ability to piece together modern data technologies into a cohesive system.

Be ready to go over:

• Pipeline Architecture – Designing ETL/ELT pipelines using Airflow, Dagster, or DBT.
• Storage Choices – When to use a Data Warehouse (BigQuery) vs. a Data Lake vs. a fast look-up store (Redis/Cassandra).
• Streaming vs. Batch – integrating Kafka or Flink for real-time needs versus batch processing.
• Data Quality – Designing automated anomaly detection and alerting for massive datasets.

Example questions or scenarios:

• "Design a system to track ad impressions and conversions for millions of concurrent users."
• "How would you architect a dashboard that shows real-time voice connection quality metrics globally?"
• "We are seeing data latency issues in our daily reporting pipeline. How would you debug and re-architect this?"

Experience & Behavioral

We want to understand how you work. This round digs into your past projects, your failures, and your leadership style.

Be ready to go over:

• Conflict Resolution – Handling disagreements with Product Managers or other engineers.
• Technical Leadership – Times you mentored others or drove a technical initiative from 0 to 1.
• Project Retro – Discussing a time you failed or brought down production, and what you learned.

The word cloud above highlights the most frequent concepts in our interviews. Notice the prominence of Python, SQL, Pipelines, and Scale. Prioritize your study time to ensure you can speak confidently about these high-frequency topics.

5. Key Responsibilities

As a Data Engineer at Discord, your daily work blends infrastructure building with business enablement.

• Pipeline Development: You will create and maintain complex, enterprise-scale data pipelines. This involves writing production-grade code (Python/SQL) to ingest structured and unstructured data from various sources, including game SDKs and third-party advertising platforms.
• Infrastructure Strategy: You will help define the technical vision for our data platform. This includes selecting the right tools (e.g., leveraging BigQuery, Dagster, or Rust for performance) and designing data models that support analytics and machine learning.
• Data Quality & Reliability: You are responsible for the health of the data. You will build monitoring systems, automated anomaly detection, and alerting infrastructure to ensure that data is accurate and available when stakeholders need it.
• Cross-Functional Leadership: You will collaborate closely with Data Scientists to implement their requirements into scalable solutions. For senior roles, you will also mentor junior engineers, conducting code reviews and pair programming to elevate the team's technical bar.

6. Role Requirements & Qualifications

We are looking for engineers who have "been there, done that" with high-volume data.

• Technical Skills (Must-Have):

  • Expert-level SQL and Python.
  • Experience with cloud data warehouses (GCP BigQuery is preferred, but AWS Redshift/Snowflake is acceptable).
  • Hands-on experience with orchestration tools like Airflow, Dagster, or DBT.
  • Experience with distributed systems and big data processing (Spark, Flink, or Kafka).

• Experience Level:

  • Typically 5+ years for Senior roles and 7+ years for Staff roles.
  • Proven history of handling massive datasets (billions of rows/petabytes).
  • Experience in Ad Tech or Marketing Tech is highly valued for Ads-specific roles.

• Soft Skills:

  • Strong communication skills to explain complex technical implementations to non-technical stakeholders.
  • A collaborative mindset with a history of mentoring peers.

• Nice-to-Have:

  • Proficiency in Rust (we use it heavily for performance).
  • Experience with containerization (Docker/Kubernetes).
  • A genuine passion for gaming or the Discord platform.

7. Common Interview Questions

The following questions are representative of what you might face. They are not an exhaustive list but serve to illustrate the patterns and difficulty level you should expect.

Technical & Coding

• "Given a table of user logins, write a query to find users who have logged in on 3 consecutive days."
• "Implement a function to flatten a deeply nested JSON structure into a tabular format."
• "How would you optimize a Python script that is running out of memory when processing a 100GB file?"
• "Write a SQL query to calculate the 'Click-Through Rate' per campaign, handling potential division by zero errors."

System Design

• "Design the data model and pipeline for a real-time 'Currently Playing' status feature for 200M users."
• "How would you ingest and deduplicate data from a third-party ad network that delivers files unpredictably?"
• "Design a data warehouse schema for storing chat logs that allows for efficient text search and retention policies."

Behavioral

• "Tell me about a time you had to compromise on technical debt to meet a product deadline."
• "Describe a situation where you identified a data quality issue that others missed. How did you fix it and prevent it from happening again?"
• "How do you handle a situation where a Data Scientist requests a feature that you know will not scale?"

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

8. Frequently Asked Questions

Q: How difficult is the coding round? The coding round is challenging but practical. We avoid obscure brain teasers in favor of problems that reflect actual data engineering tasks. However, the bar for code quality is high—we expect production-ready syntax, proper variable naming, and edge-case handling.

Q: Do I need to know Rust? While Discord is famous for using Rust, it is generally considered a "bonus" for Data Engineering roles unless specified for the Data Platform team. Proficiency in Python and SQL is the primary requirement, but a willingness to learn Rust is a plus.

Q: What is the remote work policy? Many of our engineering roles, including Data Engineering, are open to remote work (typically within specific states or time zones) or based in our San Francisco hub. The job description will specify the location requirements.

Q: How does the level calibration work? We interview candidates for a general engineering bar. If you apply for a Senior (L3) role but your system design or leadership examples don't quite meet that specific bar, we may still offer you a role at the L2 level if your technical foundations are strong.

9. Other General Tips

Know the "Modern Data Stack" Discord leverages modern tools. Familiarity with Dagster (orchestration), BigQuery (warehousing), and DBT (transformation) will help you speak our language. If you only know legacy Hadoop stacks, brush up on how these modern cloud-native tools operate.

Think about "Discord Scale" When answering design questions, always ask: "Does this work for 10 users or 200 million?" We care deeply about scalability. Mentioning partitioning, clustering, and sharding strategies in your design answers is often the difference between a "Pass" and a "Strong Hire."

Show Your Passion We are a mission-driven company. You don't have to be a hardcore gamer, but you should understand why Discord exists and how it helps people find belonging. Relate your technical answers back to the user experience whenever possible.

10. Summary & Next Steps

Joining Discord as a Data Engineer means taking ownership of infrastructure that connects the world. You will work on high-impact projects—from optimizing ad delivery to ensuring voice quality for millions of gamers. The challenges here are unique due to our massive scale and real-time nature.

To succeed, focus your preparation on SQL and Python coding fluency, distributed system design, and data modeling. Be ready to discuss how you build systems that are not just functional, but resilient and scalable. Approach the process with curiosity and confidence; we are looking for problem solvers who are ready to build the future of communication.

The salary range provided above reflects the base salary for the position. Actual compensation packages at Discord are competitive and include equity and benefits, which are significant components of the total offer. The final offer will depend on your interview performance, experience level, and location.

Good luck with your preparation. We look forward to seeing what you can build!