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Discord is seeking a Senior Data Engineer to lead technical vision and strategy for advertising product data infrastructure. You will design and own core ads data models (fact/dimension tables, canonical datasets, aggregation layers) powering delivery, measurement, targeting, attribution, and ML use cases. Key responsibilities include building and maintaining ML data infrastructure for ads ranking, delivery, and targeting—including feature development, label generation workflows, intra-day training, and ML input observability. You'll build conversion measurement pipelines and integrate third-party attribution data from Mobile Measurement Partners (Adjust, AppsFlyer, Singular), ensuring attribution accuracy across measurement surfaces.
You will architect batch and near real-time pipeline infrastructure across the ads ecosystem using BigQuery, dbt, and Dagster, pushing toward lower-latency data for ML and reporting. You'll develop data quality frameworks, monitoring systems, automated anomaly detection, and SLA infrastructure for critical ads pipelines at massive scale. The role involves proactively identifying foundational data infrastructure gaps with broad implications across ML, measurement, and reporting, then designing scalable, canonical solutions. You'll build systems from scratch in a rapidly evolving, greenfield advertising data environment, making sound architectural decisions with incomplete information while balancing short-term delivery with long-term infrastructure investment.
Cross-functional collaboration is central: you'll drive alignment across Data Science, ML Engineering, Ads Product, and GTM teams through clear narratives connecting data infrastructure decisions to business outcomes and revenue impact. You'll 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.
Required: 5+ years hands-on experience writing production code and architecting data pipelines with high-volume consumer data in advertising technology (ad delivery, ranking, targeting, identity, conversion measurement). Deep expertise in digital advertising data engineering—specifically ads delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure. Demonstrated experience building data models in greenfield or 0-to-1 environments with changing requirements and sparse documentation. Expert-level SQL and Python with ability to design performant, maintainable data models and production-quality pipeline code. Proven hands-on experience with data quality audits, monitoring systems, and automated anomaly detection for massive-scale datasets (billions+ rows), including quality frameworks for ML inputs. Strong technical communication skills and collaborative mindset with experience building trusted relationships with Data Science, ML Engineering, and Product teams.
