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Scopely Explore seeks a hands-on technical leader to design and build the data infrastructure and experimentation platforms powering mobile games at scale. This role combines deep individual technical contribution with organizational leadership—you will write production code, make foundational architectural decisions, and operate critical distributed systems while guiding the technical direction of the Data Infrastructure organization.
Key responsibilities include designing and operating scalable data infrastructure and experimentation systems for product experimentation and game-specific use cases. You will lead technical design of experiment assignment, player segmentation, exposure logging, telemetry processing, metric computation, and reproducible analysis. You'll build reliable batch and streaming data pipelines using Apache Beam, Spark, Ray, Flink, and Kafka, and design data platforms on GCP leveraging BigQuery, Apache Iceberg, Google Cloud Storage, Kubernetes, Airflow, and Terraform.
You remain hands-on throughout the development lifecycle, contributing production code and solving complex distributed computing problems. The role emphasizes AI-native development methodologies—you will develop effective methodologies for AI-assisted and agentic engineering, including context management, automated validation, evaluations, and human review. You maintain ownership of correctness, security, performance, and maintainability of all work, including AI-assisted code.
Beyond technical execution, you lead technical direction and delivery, aligning engineers and cross-functional partners around shared priorities and multi-quarter execution plans. You coach and develop engineers through mentorship and actionable feedback while fostering a collaborative, high-performing team culture. Depending on team structure, this role may include formal people-management responsibilities.
Required qualifications include extensive experience designing, building, and operating production data platforms or large-scale distributed systems. Strong programming ability in Java, Python, Go, or Rust (Java/Python especially relevant). Deep experience with cloud data infrastructure, preferably GCP and BigQuery. Production experience with distributed computing frameworks (Beam, Spark, Ray), real-time data systems (Flink, Kafka), and orchestration/infrastructure automation (Kubernetes, Airflow, Terraform). Understanding of modern storage and lakehouse architectures (Iceberg, object storage). Experience building experimentation, analytics, telemetry, or data products used by multiple teams. Knowledge of trustworthy experimentation foundations and demonstrated ability to use AI-native workflows across the full engineering lifecycle. Experience defining technical direction, driving complex cross-team initiatives, and managing/developing engineering teams.
