• Build and scale distributed data pipelines for large-scale time series, log data, and high-volume event streams. • Design and maintain reliable, high-performance Spark and Python workflows to support model training datasets. • Analyze and resolve performance bottlenecks related to latency, memory utilization, data skew, and throughput. • Improve data quality, validation processes, and reproducibility for machine learning workloads. • Partner with machine learning engineers and researchers to accelerate foundation model development. • Measure and optimize application and transaction performance in production data systems. • Collaborate cross-functionally to ensure data infrastructure aligns with evolving research and product needs.