Senior Software Engineer, Attribution

Unity Unity · Enterprise · Mountain View, CA · Templates

Senior Software Engineer to architect and implement distributed data systems for a near real-time reporting platform. The role involves designing and building high-throughput, low-latency data processing pipelines for attribution data, processing terabytes of data, and operating at the intersection of distributed systems, stream processing, and cloud-native infrastructure. Responsibilities include owning the end-to-end attribution pipeline, building real-time ad event processing services, migrating legacy systems, improving low-latency data services, building ad serving infrastructure, driving data health monitoring, and collaborating with ML and Reporting teams.

What you'd actually do

  1. Own the end-to-end attribution pipeline. You will be a core engineer on the team responsible for getting attribution data right — from the moment a user interacts with an ad to the signal landing accurately in our data warehouse and in the hands of our ML and Reporting teams. This means owning a portfolio of high-throughput, latency-sensitive services and streaming pipelines that operate at the center of Unity's mobile advertising infrastructure.
  2. Build and operate real-time ad event processing. You will design, build, and operate the online services that receive ad interaction events from mobile SDKs and other partners, process terabytes of data at scale — including enrichments, transformations, and privacy operations — and deliver signals to external and internal partners in real time.
  3. Lead our architectural modernization. A significant part of your work will be an active migration, replacing a legacy event-handling layer with a modern gRPC-based service. This is greenfield development within a production system, requiring careful design with backfill and failover behavior while preserving data accuracy throughout the cutover.
  4. Improve low-latency data services. You will improve the caching layer that serves data on every ad request within strict latency budgets, and own the persistence layer that links auction, fill, placement, and gamer identities together across the ad lifecycle.
  5. Build and maintain critical ad serving infrastructure. You will build and own multiple services that support ad serving — including negative targeting and block listing — across the team's service portfolio. Responsibilities include logging, tracing, and authentication; a Kafka-backed caching framework; health check and readiness probe infrastructure; and Docker-based integrations.

Skills

Required

  • Strong foundation in distributed systems and systems design
  • Hands-on experience building and operating large-scale data processing systems
  • Deep understanding of streaming concepts: exactly-once semantics, watermarking and event-time processing, stateful stream processing, checkpointing and recovery, and backpressure handling
  • Production experience with frameworks such as Apache Flink, Spark, Kafka, or similar technologies
  • Proficiency in Python, Java, or Scala
  • Experience with workflow orchestration tools such as Airflow for stream and batch coordination
  • Strong understanding of cloud-native architectures and distributed infrastructure including Kubernetes, containerization, and cloud platforms

Nice to have

  • Experience migrating legacy systems to modern architectures in production environments

What the JD emphasized

  • high-throughput
  • low-latency
  • real-time
  • terabytes of data
  • distributed systems
  • stream processing
  • cloud-native infrastructure
  • production ownership
  • attribution pipeline
  • ad event processing
  • data accuracy
  • low-latency data services
  • ad serving infrastructure
  • data health monitoring
  • ML modeling
  • Reporting
  • large-scale data processing systems
  • streaming concepts
  • exactly-once semantics
  • watermarking and event-time processing
  • stateful stream processing
  • checkpointing and recovery
  • backpressure handling
  • Apache Flink
  • Spark
  • Kafka
  • workflow orchestration tools
  • cloud-native architectures
  • distributed infrastructure
  • Kubernetes
  • containerization
  • cloud platforms