Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Designs and operates scalable ML infrastructure, including training, serving, inference, orchestration, and deployment pipelines. Optimizes GPU and CPU workloads, manages cloud clusters, implements observability and SLOs, and secures automated CI/CD and infrastructure-as-code workflows. Partners with research and AI engineering teams to productionize models, while building reusable tooling, templates, and runbooks. The role also supports low-latency model serving, data quality, model promotion, and cost optimization.