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
Design and own core ads data models: fact/dim tables, canonical datasets, and aggregation layers that power delivery, measurement, targeting, attribution, and ML use cases.
Build and maintain the ML data that enables ads ranking, delivery, and: targeting - including feature development, label generation workflows, intra-day training, and ML input observability to catch data quality issues before they degrade model performance.
Build conversion measurement pipelines and integrate third-party attribution: data - including Conversion Attribution and Mobile Measurement Partner (MMP) integrations (Adjust, AppsFlyer, Singular) - ensuring attribution accuracy and data parity across measurement surfaces.
Build batch and near real-time pipeline infrastructure across the ads: ecosystem - pushing toward lower-latency data for ML and reporting use cases on our BigQuery + dbt + Dagster stack. Partnering with Data Platform on launch and success of new data processing engines to support low latency requirements..
Develop data quality frameworks, monitoring systems, automated anomaly: detection, and SLA infrastructure for critical ads pipelines at massive scale.
Proactively identify foundational data infrastructure gaps: including those with broad implications across ML, measurement, and reporting - and design scalable, canonical solutions that multiple teams can depend on.
What they're looking for
- Build systems from scratch in a rapidly evolving, greenfield advertising data: environment - making sound architectural decisions with incomplete information and balancing short-term delivery with long-term infrastructure investment.
- Drive alignment across Data Science, ML Engineering, Ads Product, and GTM: teams through clear narratives that connect data infrastructure decisions to business outcomes and revenue impact.
- Mentor engineers through technical challenges, code and design reviews, and: ownership of complex projects - contributing to the culture and engineering standards of the Data Engineering team team.
- + years of hands-on experience writing production code and architecting data: pipelines with high-volume consumer data in advertising technology domains (ad delivery, ranking, targeting, identity, conversion measurement).