Description:
- 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.
8 Feb 2026;
from:
linkedin.com