Staff Data Engineer- Ads reporting

Unity Technologies•San Francisco, CA

About The Position

Unity is building a robust, near real-time reporting platform that powers critical analytics and decision-making across our ecosystem. We are looking for a Data Engineer to help architect and implement the distributed data systems that drive this platform at scale. In this role, you’ll design and build high-throughput, low-latency data processing pipelines that power reporting used by internal teams and external customers. You’ll operate at the intersection of distributed systems, stream processing, and cloud-native infrastructure — ensuring correctness, reliability, and scalability in a high-volume production environment. This is a high-impact role where engineering rigor, architectural clarity, and production ownership matter.

Requirements

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, 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 (e.g., Airflow) for stream and batch coordination.
Strong understanding of cloud-native architectures and distributed infrastructure (Kubernetes, containerization, cloud platforms).
Sufficient knowledge of English to have professional verbal and written exchanges in this language.

Responsibilities

Design and implement near real-time data pipelines and reporting infrastructure.
Architect distributed stream and batch processing systems using technologies such as Apache Flink, Spark, and Airflow.
Build and maintain data processing frameworks that handle large-scale event ingestion and transformation with strong correctness guarantees.
Ensure production-grade reliability, observability, and operability across distributed systems.
Define and enforce data processing semantics including: Exactly-once processing, Event time vs. processing time handling, Stateful stream management, Backpressure and fault tolerance strategies.
Collaborate cross-functionally with data consumers, product, and infrastructure teams to define scalable reporting solutions.
Contribute to long-term platform architecture, setting engineering standards for performance, resilience, and maintainability.