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.

End-to-end lakehouse pipeline for retail data: Postgres to Databricks (query-based incremental ingestion), dbt (incremental models, SCD2, metadata-driven OBT), Airflow orchestration, S3 secondary ingestion, CI-gated on dbt tests.

End-to-end data warehouse for multi-country market-share analytics (FMCG / nutrition) across two markets — Spain (ES) and Portugal (PT) — built on a medallion architecture in Snowflake, feeding Power BI dashboards.

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

End-to-end Retail Sales ETL Pipeline using Python, PostgreSQL, Apache Airflow, Docker, Star Schema Data Warehouse Modeling, KPI Generation, and Incremental Data Loading.

My first data warehousing project in Databricks SQL a layered pipeline (staging → transformation → core) modeled into a star schema, with incremental loading and SCD Type 1 using MERGE INTO.

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

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.

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

A Databricks data engineering project simulating a full medallion architecture (Bronze → Silver → Gold) for e-commerce sales data, featuring incremental loading, a Kimball-style star schema, and SCD Type 1 & Type 2 implementations using PySpark and Delta Lake MERGE INTO.

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.

Production-ready ETL Pipeline that extracts GitHub repositories, performs incremental loading into PostgreSQL, sends email notifications, and runs automatically using APScheduler.

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

End-to-end ETL Data Pipeline for Olist E-Commerce. Spark ingestion, Parquet transformation, Data Warehousing (Galaxy Schema) with SCD Type 2, incremental PostgreSQL loading, daily scheduling, Analytical SQL queries, and persistent Audit Logging.

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.

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

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.