Staff Data Engineer

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 can make data access more useful and dependable.
• Work closely with upstream Data Platform and downstream Data Science partners to align on interfaces, data contracts, and operational expectations; communicate trade-offs and risks clearly to technical and non-technical stakeholders.
• Mentor data engineers through design and code reviews, technical guidance, and ownership of complex projects. Raise the bar for engineering quality and help teams adopt consistent data modeling and documentation practices.

What You Should Have

• 7+ years of experience building and operating production data pipelines and data products, including significant hands-on ownership of architecture and implementation.
• Expert-level SQL and strong Python skills, with experience writing maintainable, tested production code.
• A track record of designing robust data models from large-scale event, product, or transactional data and making them usable for analytics, reporting, and experimentation.
• Experience setting technical direction across complex projects, identifying systemic data problems, and delivering solutions that multiple teams can rely on.
• Strong data quality instincts, including experience with testing, monitoring, data freshness, documentation, and the investigation of metric discrepancies.
• Experience with modern data engineering tools and workflows (for example, dbt, BigQuery, Dagster, or equivalent), version control, and CI/CD practices.
• Experience partnering with Data Science, Product, Finance, and Engineering to turn ambiguous business questions into clear metric definitions and dependable data products.
• Excellent communication and collaboration skills, with the ability to build alignment, explain technical trade-offs, and earn trust across teams.
• A desire to work with thoughtful, passionate people who care deeply about solving challenging problems to improve Discord.

Bonus Points:

• Passion for Discord or online communities
• Experience with subscription, payments, e-commerce, virtual currency, or other consumer monetization data.
• Experience building data products for experimentation, causal measurement, or product launch evaluation.
• Experience developing a semantic layer, metrics store, or self-service analytics platform.
• Experience with streaming or near-real-time data systems, or improving the reliability and observability of critical pipelines.
• Experience with AI-assisted analytics or tools that help teams safely discover and use trusted data.
• Passion for Discord, gaming, or online communities.