DATA ENGINEERING
PLATFORM / PIPELINE / MODEL
I build the path from raw operational data to governed, queryable, decision-grade systems. My strongest lane is Microsoft Fabric, Databricks, Azure, PySpark, SQL, Power BI, semantic modeling, and production platform delivery.
Separate raw recovery from trusted entities so quality and ownership are visible.
Current delivery context: enterprise data engineering with ownership across source integration, modeling, reporting output, and production support.
Operating Model
The work is not just writing transformations. It is owning the path: source behavior, ingestion reliability, layered modeling, release discipline, semantic output, and production support.
> ARCHITECTURE BLUEPRINT
Understand source ownership, refresh behavior, keys, and failure modes before building.
> ANONYMIZED ARCHITECTURE DIAGRAMS
Replace brittle warehouse logic with governed lakehouse layers and owned semantic output.
Convert machine signals into operational alerts before issues become manual firefighting.
Existing warehouse logic and reporting dependencies mapped before migration.
> FABRIC / DATABRICKS PIPELINE SCHEMATIC
Distributed transformations, jobs, notebooks, and scalable engineering logic.
> MISSION REPLAY
Legacy TimeXtender platform was blocking scale and modernization.
Led migration to Microsoft Fabric: data architecture, environments, and deployment pipelines. Result: Legacy platform fully replaced by a stable, scalable Fabric solution.
Stack & Skill Tree
Production-minded data engineering across cloud platforms, orchestration, transformations, semantic models, reporting, and delivery process.
> TOOL INVENTORY
Lakehouse, PySpark, Workflows
> ACHIEVEMENTS / CERTS
Validates production data engineering fundamentals on Databricks: lakehouse concepts, jobs, Spark, Delta, and pipeline delivery.