Staff Data Engineer, Ads

About the role

Discord is seeking an experienced Data Engineer to contribute to our advertising product data initiatives. In this role, you will shape the technical direction for ads data engineering, supporting machine learning efforts by creating and managing complex data pipelines and datasets. You will also guide cross-functional projects to enhance our advertising products through data-driven insights and mentor other engineers.

Key facts

What you'll do

• Create and manage core ads data models, including fact and dimension tables, standardized datasets, and aggregation layers that support ad delivery, measurement, targeting, attribution, and machine learning applications.
• Develop and maintain the data infrastructure essential for ads ranking, delivery, and targeting, encompassing feature creation, label generation processes, intra-day model training, and observability for ML inputs to detect data quality issues.
• Construct pipelines for conversion measurement and integrate data from third-party attribution providers, such as Mobile Measurement Partners (MMPs) like Adjust, AppsFlyer, and Singular, ensuring accuracy and consistency in measurement.
• Build infrastructure for both batch and near real-time data pipelines across the ads ecosystem, aiming for reduced latency for ML and reporting needs using a BigQuery, dbt, and Dagster stack. Collaborate with the Data Platform team on new data processing engines to meet low-latency demands.
• Establish data quality frameworks, monitoring systems, automated anomaly detection, and service level agreement (SLA) infrastructure for critical, large-scale ads pipelines.
• Identify fundamental gaps in data infrastructure that impact ML, measurement, and reporting, and design scalable, standardized solutions for broad team adoption.
• Construct systems from the ground up in a dynamic, new advertising data environment, making informed architectural choices with incomplete information and balancing immediate delivery needs with long-term infrastructure investment.
• Foster alignment among Data Science, ML Engineering, Ads Product, and Go-To-Market teams by clearly communicating how data infrastructure decisions connect to business objectives and revenue.
• Mentor engineers by guiding them through technical challenges, providing feedback on code and designs, and overseeing complex projects, thereby contributing to the Data Engineering team's standards and culture.

Requirements

• A minimum of 5 years of practical experience in writing production code and architecting data pipelines for high-volume consumer data within advertising technology, covering areas like ad delivery, ranking, targeting, identity, and conversion measurement.
• Profound knowledge of digital advertising data engineering, particularly in ad delivery, conversion measurement, attribution pipelines, or ML feature data infrastructure. Experience with conversion data, APIs, MMP integrations, or identity graph infrastructure is highly valued.
• Proven ability to build data models in a new or foundational environment where requirements evolve frequently, documentation is limited, and architectural decisions are made with incomplete data.
• Expert proficiency in SQL and Python, with a strong capacity for designing efficient, maintainable data models and writing production-ready pipeline code.
• Demonstrated experience implementing data quality audits, monitoring systems, and automated anomaly detection for datasets exceeding billions of rows, including quality frameworks specifically for ML inputs.
• Strong technical communication skills, enabling you to drive consensus, influence priorities, and gain buy-in from technical stakeholders.
• A collaborative approach and strong cross-functional understanding, with a history of building reliable working relationships with Data Science, ML Engineering, and Product teams.

Nice to have

• Experience with data visualization and dashboarding tools like Looker or Tableau.
• Experience designing data architecture to support diverse use cases, including reporting, ad-hoc analysis, and experimentation.
• Familiarity with Data AI tools to enhance self-service capabilities for users.
• Prior experience building near real-time or streaming pipeline infrastructure (e.g., Kafka, Spark Streaming) in addition to batch processing.

Skills & tools

• SQL
• Python
• BigQuery
• dbt
• Dagster

Practical notes

• Visa sponsorship is not available for this role.
• Discord is committed to inclusion and providing reasonable accommodations during the interview process. If you require accommodations, please inform your recruiter.
• Please review Discord's Applicant and Candidate Privacy Policy for details on data collection and usage.

About the company

Discord is used by over 200 million people every month for many different reasons, but there's one thing that nearly everyone does on our platform: play video games. Over 90% of our users play games, spending a combined 1.5 billion hours playing thousands of unique titles on Discord each month.