You design, build, and operate scalable data infrastructure and experimentation systems for both general product experimentation and game-specific use cases
You lead the technical design of capabilities such as experiment assignment, player segmentation, exposure logging, telemetry processing, metric computation, guardrails, and reproducible analysis
You build reliable batch and streaming data pipelines using technologies such as Apache Beam, Apache Spark, Ray, Apache Flink, and Apache Kafka
You design data platforms on GCP using technologies such as BigQuery, Apache Iceberg, Google Cloud Storage, Kubernetes, Airflow, and Terraform
You remain hands-on throughout the development lifecycle, contributing production code and solving complex problems in distributed computing, streaming, orchestration, and storage
You develop effective methodologies and guardrails for AI-assisted and agentic engineering, including context management, automated validation, evaluations, human review, and responsible adoption
You use AI-native workflows across your end-to-end responsibilities, including architecture exploration, implementation, testing, code review, documentation, debugging, incident response, and platform operations
You maintain ownership of the correctness, security, performance, and maintainability of all work produced with AI assistance
You evaluate new technologies pragmatically and apply them where they materially improve engineering velocity, platform reliability, or experimentation quality
You lead the technical direction and delivery of the Data Infrastructure, aligning engineers and cross-functional partners around shared priorities, architectural decisions, trade-offs, and multi-quarter execution plans
You coach and develop engineers through mentorship, actionable feedback, and technical guidance, while fostering a collaborative, high-performing team culture and remaining hands-on in architecture and critical implementation