Senior Data Engineer, Ads

Roblox Roblox · Consumer · San Mateo, CA · Software Engineering

Senior Data Engineer to build the next generation of Ad Data and ML feature infrastructure. This role will architect and build foundational data pipelines, real-time streaming systems, and scalable feature computation frameworks to support ads personalization, ranking, and measurement. The work will enable rapid experimentation, empower ranking models, and support personalized ad delivery at massive scale. Collaboration with ML, backend, and analytics engineers is key.

What you'd actually do

  1. Build high-performance data pipelines and real-time streaming systems that support both online and offline ML features, training data and statistical computation at scale.
  2. Design and scale robust data infrastructure for powering retrieval and ranking of various ads systems across Roblox
  3. Ensure data systems are reliable, observable, privacy-compliant, and performant under heavy concurrency and high throughput.
  4. Partner with ML and backend engineers to define scalable interfaces and enable rapid iteration for model development and experimentation.
  5. Partner with our Data Platform and Data Infra teams to build our high quality data processing workflows.

Skills

Required

  • 7+ years of work experience building production-grade, scalable, and reliable data systems
  • Solid programming skills in languages such as Python
  • strong knowledge of SQL
  • Strong expertise in developing large-scale data pipelines (batch and streaming)
  • big-data processing technologies such as Spark, Apache Druid, Flink, Ray, Kubeflow, etc
  • Excellent cross-functional collaboration skills
  • Data-driven focus for quality metrics and monitoring
  • Approach data as a product—prioritizing quality, discoverability, and reusability

Nice to have

  • Knowledge about advertising and the technology which powers it is preferred
  • Experience building data pipelines at TB+ scale.
  • Experience leveraging AI tooling to build simplified, human-centric experiences for internal customers.

What the JD emphasized

  • ML feature infrastructure
  • ranking models
  • ML features, training data

Other signals

  • ML feature infrastructure
  • ranking models
  • personalized ad delivery
  • ML features, training data