• Design scalable conceptual, logical, and physical data models supporting OLTP, OLAP, real-time analytics, and ML workloads • Architect modular, domain-driven data structures for multi-domain analytics • Apply modern modeling techniques, including 3NF, Dimensional Modeling, Data Vault, Medallion Architecture, and Data Mesh principles • Define canonical models, conformed dimensions, and enterprise reference datasets • Architect real-time ingestion and event-driven pipelines using Kafka, Kinesis, Pulsar, or Azure Event Hub • Design low-latency, high-throughput streaming architectures for operational and analytical use cases • Design ML-ready datasets, feature stores, and reproducible data pipelines • Architect for drift detection, data quality monitoring, lineage visibility, retraining workflows, and model governance • Design scalable architectures using Snowflake, Databricks, or other cloud-native platforms