- 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.