•Data pipeline development — Design, build, and maintain Bronze, Silver, and Gold lakehouse pipelines that power member profiles, clinical signals, engagement data, and decision intelligence use cases.
•Feature engineering — Develop and maintain Gold-layer feature tables and reusable data products that support model training, model scoring, reinforcement learning, and real-time decisioning.
•Batch and streaming ingestion — Build and support batch and real-time ingestion pipelines using Databricks, Spark, Delta Lake, and event-driven data architectures.
•Data quality engineering — Implement data quality validation, reconciliation, monitoring, alerting, and testing controls to ensure trusted and production-ready data assets.
•Data modeling — Design scalable data models that balance usability, governance, performance, and long-term maintainability.
•Performance optimization — Tune Spark workloads, storage strategies, partitioning schemes, and query performance to improve efficiency, scalability, and cost management.
•Data governance — Follow established standards for data lineage, security, PHI handling, auditability, and regulatory compliance.
•Platform integration — Partner with Decision Intelligence, AI Engineering, and Platform Engineering teams to ensure high-quality data is available across the NBA ecosystem.
•Operational support — Diagnose and resolve pipeline failures, data quality issues, and production incidents to maintain reliable platform operations.