1 month ago
Base Salary
$221k - $245k/yr
Responsibilities
- Design and own fact/dimension tables, canonical datasets, and aggregation layers for advertising delivery, measurement, targeting, attribution, and ML use cases.
- Build and maintain ML data systems, including feature development, label generation, intra-day training workflows, and ML input observability.
- Develop conversion measurement pipelines and integrate third-party attribution platforms including Adjust, AppsFlyer, and Singular.
- Build batch and near-real-time pipeline infrastructure on the BigQuery, dbt, and Dagster stack.
- Develop data quality frameworks, monitoring systems, automated anomaly detection, and SLA infrastructure for large-scale ads pipelines.
- Identify data infrastructure gaps and design scalable canonical solutions for ML, measurement, and reporting teams.
- Build greenfield advertising data systems and make architecture decisions amid evolving requirements and incomplete information.
- Drive alignment across Data Science, ML Engineering, Ads Product, and GTM teams by connecting infrastructure decisions to business outcomes.
- Mentor engineers through technical challenges, code and design reviews, and complex project ownership.
Requirements
- 5+ years of hands-on experience writing production code and architecting high-volume data pipelines in advertising technology domains.
- Deep expertise in advertising data engineering, including ads delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure.
- Experience building data models in greenfield or 0-to-1 environments with changing requirements and incomplete documentation.
- Expert-level SQL and Python skills for designing performant data models and production-quality pipeline code.
- Hands-on experience with data quality audits, monitoring systems, and automated anomaly detection for massive-scale datasets.
- Strong technical communication and stakeholder alignment skills.
- Experience collaborating with Data Science, ML Engineering, and Product teams.
- Experience with data visualization and dashboarding tools such as Looker or Tableau is a bonus.
- Experience designing data architecture for reporting, ad hoc analysis, and experimentation is a bonus.
- Experience with near-real-time or streaming pipeline infrastructure such as Kafka or Spark Streaming is preferred.
- Passion for Discord or gaming communities and experience with Data AI tools are bonus qualifications.
Benefits
- Full-time position with equity and benefits.
- US base salary range and compensation details are provided in the posting.
- Discord provides reasonable accommodations during the interview process.
Tech Stack
Categories
Data Engineering
About Discord
Discord builds a voice, video, and text chat platform organized around servers for gaming and interest-based communities. It monetizes through Nitro subscriptions, server subscriptions, and digital goods, and offers a developer platform for bots and apps. Founded in 2015 and headquartered in San Francisco, it is privately held and used by over 200 million people monthly.