Discord

Discord

Staff Data Engineer, Ads

Remote (U.S.) · Staff+ · Full-time

Sponsorship not specified$248k-$310kDetected 5 days ago
SQLBigQueryKafkaMachine LearningSparkdbtData EngineeringData ScienceData VisualizationAccount ManagementLeadershipCommunication

About the role

  • Discord seeks a seasoned technical leader to join our Data team as a Staff Data Engineer, focusing on our advertising products.
  • This role works heavily with Data Science, Machine Learning, product, sales, and account management teams.
  • If leading technical innovation, architecting scalable solutions, and empowering teams through data excites you, we encourage you to make a move!

Responsibilities

  • 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.

Requirements

  • 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.
  • 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.
  • 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.

Compensation

  • The US base salary range for this full-time position is $248,000 to $310,000 + equity + benefits.
  • Our salary ranges are determined by role and level.
  • Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

Company info

  • Working with Data AI tools to establish greater self service utility for your customers.
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This listing is sourced directly from Discord's careers page and normalized into a canonical job model.