Responsibilities

• 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.
• 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.

Requirements

• 5+ 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).
• Deep expertise in digital advertising data engineering - specifically in ads delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure. Experience with Conversion Data and APIs, MMP integrations, or identity graph infrastructure is strongly valued.
• Demonstrated experience building data models in a greenfield or 0-to-1 environment where requirements change frequently, documentation is sparse, and architectural decisions are made with incomplete information.
• Expert-level SQL and Python. Strong ability to design performant, maintainable data models and write 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 designed for ML inputs.
• Strong technical communication skills with the ability to drive alignment, influence prioritization, and earn adoption from technical stakeholders.
• Collaborative mindset and strong cross-functional instincts with experience building trusted working relationships with Data Science, ML Engineering, and Product teams.

Preferred

• Passion for Discord or gaming communities
• Experience with data visualization and dashboarding technologies (Looker, Tableau, or similar)
• Experience with designing data architecture to power a variety of use cases, including reporting (internal and external), adhoc analysis, experimentation.
• Working with Data AI tools to establish greater self service utility for your customers.
• Experience building near real-time or streaming pipeline infrastructure (e.g., Kafka, Spark Streaming, or equivalent) in addition to batch processing is preferred.