Staff Data Engineer

Discord•San Francisco, CA

10h•$248,000 - $310,000

About The Position

Discord is hiring a Staff Data Engineer to join its Data Engineering team and partner with Consumer Revenue. The team’s mission is to make it easy for users to discover, understand, buy, and keep Discord Premium Products. This role sets technical direction for data systems supporting Nitro, Shop, server monetization, gifting, and other consumer revenue experiences. The engineer will build reliable data foundations that help teams understand performance, run experiments, and make product and business decisions. The role works closely with Data Science, Product, Finance, Data Platform, and engineering partners. It leads complex data engineering work, improves shared data products, and supports the growth of a high-velocity consumer business. The position is full-time and includes eligibility for equity and benefits. The stated base salary range is $248,000–$310,000 USD.

Requirements

At least 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, including experience writing maintainable, tested production code.
Track record designing robust data models from large-scale event, product, or transactional data and making them useful 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 investigating metric discrepancies.
Experience with modern data engineering tools and workflows such as dbt, BigQuery, Dagster, or equivalent, as well as 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, including the ability to build alignment, explain technical trade-offs, and earn trust across teams.
Desire to work with thoughtful, passionate people solving challenging problems to improve Discord.

Nice To Haves

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.

Responsibilities

Set technical direction for Consumer Revenue data engineering by identifying foundational gaps, defining scalable architectures and standards, and leading 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, documented, and useful across 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, balancing freshness, reliability, cost, and maintainability across batch and lower-latency processing where appropriate.
Partner with Data Science, Product, Finance, and engineering to define and operationalize business metrics, including subscriber, revenue, booking, retention, and product usage measures, and maintain consistent definitions across reporting and experimentation.
Strengthen data foundations for product work and revenue growth by turning ambiguous questions into robust models, metrics, and analytical capabilities that support opportunity sizing, launch evaluation, and experimentation.
Establish data quality, testing, monitoring, and documentation practices for owned data assets, with 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 guidance; contribute to AI-assisted analytics workflows where they can make data access more useful and dependable.
Align with upstream Data Platform and downstream Data Science partners on interfaces, data contracts, and operational expectations; communicate trade-offs and risks to technical and non-technical stakeholders.
Mentor data engineers through design and code reviews and technical guidance, lead complex projects, and promote engineering quality and consistent data modeling and documentation practices.