The opportunity
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…
What you'll do
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.
What they're looking for
- 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.
- + years of experience building and operating production data pipelines and: data products, including significant hands-on ownership of architecture and implementation.
