• Collaborate with ML engineers and data platform teams to design, implement, and maintain the feature store architecture for both offline (batch) and online (real-time) use cases. • Identify, define, and engineer high-quality features from various raw data sources (databases, APIs, streaming data) using statistical analysis and domain knowledge. • Partner with database administrators, clinical experts, data scientists, ML engineers, and business stakeholders to promote feature reuse, define governance standards, track feature lineage, and ensure data consistency across models. • Build and optimize ingestion and transformation pipelines using distributed data frameworks to populate the feature store, ensuring data accuracy, reliability, and freshness. • Generate training and testing datasets from the feature store and work with ML engineers to ensure seamless feature serving for model inference in production environments. • Develop monitoring and alerting frameworks to track feature data quality, integrity, and latency, proactively identifying and resolving issues. • Document feature definitions, data sources, and usage best practices, and effectively communicate complex technical concepts and insights to technical and non-technical audiences.