About this role
Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games.
Discord is looking for a Staff Data Engineer to join our Data Engineering team and partner with Consumer Revenue. Our mission is to make it easy for all users to discover, understand, buy, and keep Premium Products on Discord. In this role, you will set technical direction for the data systems that support Nitro, Shop, server monetization and gifting, and other consumer revenue experiences. You will build reliable data foundations that help teams understand performance, run experiments, and make better product and business decisions.
You’ll work closely with Data Science, Product, Finance, Data Platform, and engineering partners. If you’re excited to lead complex data engineering work, raise the quality of shared data products, and help a high-velocity consumer business grow, read on.
What You'll Be Doing
• Set technical direction for Consumer Revenue data engineering: identify foundational gaps, define scalable architectures and standards, and lead high-impact initiatives from design through adoption.
• Design and own canonical data models and datasets for consumer monetization, including subscriptions and subscriber states, purchases and payments, Shop, gifting, and Orbs. Make these assets reliable, well-documented, and useful across product analytics, financial reporting, experimentation, and decision-making.
• Build and evolve production data pipelines that transform source events and transactional data into accurate, trusted data products. Balance freshness, reliability, cost, and maintainability across batch and, where the use case calls for it, lower-latency processing.
• Partner with Data Science, Product, Finance, and engineering teams to define and operationalize important business metrics—including subscriber, revenue, booking, retention, and product usage measures—and ensure definitions are consistent across reporting and experimentation.
• Strengthen the data foundations behind high-velocity product work and revenue growth. Translate ambiguous questions into robust models, metrics, and analytical capabilities that help teams size opportunities, evaluate launches, and learn from experiments.
• Establish data quality, testing, monitoring, and documentation practices for the data assets you own. Pay particular attention to accuracy, freshness, auditability, and edge cases in subscription, payment, and commerce data.
• Improve self-service analytics by maintaining a discoverable semantic layer, reusable data models, and clear guidance that let partner teams answer common questions with less bespoke support. Contribute to AI-assisted analytics workflows where they