Senior Data Engineer, Ads
Discord is hiring a Senior Data Engineer focused on advertising data: designing ads data models, building ML feature/label and conversion-attribution pipelines, and developing batch and near real-time infrastructure on a BigQuery + dbt + Dagster stack. The role partners with Data Science, ML, and product teams and mentors fellow engineers in the San Francisco Bay Area.
- Compensation
- $220.5K–$245K / yr
- Location
- Remote (U.S.)
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5+ years of hands-on experience writing production code and architecting data pipelines with high-volume consumer data in advertising technology domains
- Deep expertise in digital advertising data engineering (ads delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure)
- Demonstrated experience building data models in a greenfield or 0-to-1 environment
- Expert-level SQL and Python
- Proven hands-on experience with data quality audits, monitoring systems, and automated anomaly detection for massive-scale datasets (billions+ rows)
- Strong technical communication skills with the ability to drive alignment and influence prioritization
- Collaborative mindset and strong cross-functional instincts with Data Science, ML Engineering, and Product teams
Nice to have
- Passion for Discord or gaming communities
- Experience with data visualization and dashboarding technologies (Looker, Tableau, or similar)
- Experience designing data architecture for reporting, adhoc analysis, and experimentation
- Experience working with Data AI tools to establish self-service utility
- Experience building near real-time or streaming pipeline infrastructure (e.g., Kafka, Spark Streaming)
Full description
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 making it easier and more fun for people to hang out before, during, and after playing games.
Discord seeks a seasoned Data Engineer, focusing on our advertising product data. In this role, you will drive technical vision and strategy for ads data engineering in support of our ML initiatives while building and maintaining sophisticated data pipelines, datasets, and analytical tools. You will lead cross-functional initiatives to transform our advertising products through data-driven insights and mentor fellow engineers to deliver exceptional results.
This role works heavily with Data Science, Machine Learning, and product. If leading technical innovation, architecting scalable solutions, and empowering teams through data excites you, we encourage you to make a move!
What You'll Be Doing
- 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.
- 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.