REVIEW BRIEF

Databricks Platform Proficiency

Doeon Kim — Demonstrated capabilities across the Databricks Lakehouse Platform

Flagship: Lakehouse Contract Lab

Production-grade Spark + Delta Lake medallion pipeline with data contracts, schema enforcement, and Snowflake export.

Component	Detail
Architecture	Bronze / Silver / Gold medallion layers with Delta Lake
Data Contracts	Schema validation, quality checks, SLA enforcement
Processing	PySpark transformations, incremental loads, merge operations
Export	Snowflake warehouse integration for cross-platform analytics
Governance	Column-level lineage, data quality metrics, contract versioning

Nexus-Hive — Multi-agent NL-to-SQL copilot with Databricks connector, governed analytics, and multi-warehouse abstraction.
enterprise-llm-adoption-kit — LLM governance toolkit with eval pipelines suitable for MLflow integration and Databricks model serving.
tool-call-finetune-lab — QLoRA fine-tuning pipeline compatible with Databricks GPU clusters and MLflow experiment tracking.

Category	Skills
Core Platform	Workspaces, clusters, notebooks, Unity Catalog, DBFS
Data Engineering	Delta Lake, Spark SQL, structured streaming, Auto Loader
ML/AI	MLflow, model serving, feature store, fine-tuning on GPU
Architecture	Medallion layers, data contracts, lakehouse design patterns
Governance	Unity Catalog, data lineage, access controls, quality monitoring

Databricks Certified Platform Architect — Validated expertise in lakehouse architecture, Unity Catalog governance, and production workload design.