Staff Data Engineer, Ads

Requisitos y responsabilidades

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What You'll Be Doing

Provide technical leadership for Discord's ads data infrastructure - setting architectural direction, establishing engineering standards, and enabling data science, ML, and product teams to build on reliable, well-documented data foundations.
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 infrastructure that enables ads ranking, delivery, and targeting - including feature development, label generation workflows, intra-day training dataset construction, 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.
Design and maintain identity resolution infrastructure and audience pipelines for privacy-compliant targeting and Custom Audiences - with a clear understanding of the governance and regulatory constraints involved.
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.

What you should have

7+ 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.
Experience building near real-time or streaming pipeline infrastructure (e.g., Kafka, Spark Streaming, or equivalent) in addition to batch processing is preferred.
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.
Ability to independently identify foundational data infrastructure gaps and design org-wide canonical solutions - not just execute on identified work.
Strong technical communication and data-driven storytelling skills with the ability to drive alignment, influence prioritization, and earn adoption from technical and non-technical stakeholders.

Bonus Points

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.

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