Set up and run the Data Modeling COE : standards, review gates, reusable templates, and knowledge assets. Define and enforce modeling conventions , versioning, and operating model (intake → design → review → sign‑off). Drive data governance —cataloging, lineage, policy‑based access, encryption/tokenization, and compliance readiness. Lead logical and physical DB design; produce ER diagrams and schema diagrams ; maintain PTM (physical technology model) across RDBMS and NoSQL. Propose and implement re‑structuring of legacy schemas for scalability, resiliency, and cost/performance optimization. Architect multi‑tenant strategies (schema/table/row‑level isolation) and workload isolation. Define end‑to‑end migration approaches (assessment → design → build → cutover → validation) across RDBMS ↔ NoSQL and cloud platforms. Orchestrate CDC/ETL/ELT and integrations (e.g., ADF, Glue, Kafka/NiFi, Logic Apps, Databricks). Establish reconciliation, golden‑record checks, phased cutover plans, and rollback strategies. Lead performance tuning (indexing/partitioning, query plan analysis, caching) and Spark optimization to address skew, partitioning, and storage formats (Parquet/Delta). Define SLA‑backed observability and capacity planning. Automate repetitive tasks and pipeline scaffolding to reduce manual intervention across tech stacks. Implement CI/CD for data pipelines, automated quality gates, and IaC for data platforms.
