Designed a production-grade Azure Data Engineering project centered on Azure Data Factory. Built dynamic, metadata-driven pipelines to ingest data from on-prem systems, REST APIs, and Azure SQL into ADLS Gen2 using Medallion Architecture, incremental loading, and enterprise-scale orchestration patterns.

Complete Data Vault 2.0 (DV2) data warehouse implementation with Medallion Architecture on SQL Server. 67 objects, 60 SSIS packages, watermark-based incremental ETL.

Improve app performance by avoiding nested lists in Syncfusion® .NET MAUI ListView. Use grouping, DataTemplateSelector, and expand/collapse with tuning for smooth scrolling.

End-to-End Data Engineering Pipeline using Snowflake, dbt, AWS, and Medallion Architecture (Bronze, Silver, Gold) with Incremental Models, Snapshots, Macros, and Data Quality Testing.

End-to-end PySpark ETL pipeline with incremental loading, partitioned Parquet storage and audit logging for production-style data processing.

This project pulls historical and forecast weather data for multiple cities, cleans and transforms it, performs quality checks, and stores the results in tidy daily and monthly summary datasets.

Scalable and resumable Python scraper for extracting large datasets from paginated REST APIs.

Fibbie Banks is a fictional retail financial institution created as a comprehensive case study to design, build, and implement a modern, production‑grade data warehouse solution. This project demonstrates end‑to‑end data engineering best practices.

End-to-end data engineering and business intelligence platform built using Databricks, PySpark, SQL, Delta Lake, and Power BI with incremental ETL pipelines and Medallion Architecture.

Discover how Syncfusion® .NET MAUI List View outperforms Collection View with features like fast virtualization, sticky group headers, swipe, drag‑and‑drop, and incremental loading.

Incremental ETL Pipeline with Star Schema Data Warehouse using Python and PostgreSQL