End-to-end retail lakehouse on Databricks: dual-path ingestion from PostgreSQL and S3, dbt-modeled galaxy schema with SCD2 history, Airflow orchestration with parallel DAG execution, CI-gated dbt tests, and a dual consumption layer via Power BI and a Genie Agent.
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 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.
End-to-end analytics platform on Microsoft Fabric — metadata-driven pipelines, incremental loading, SQL Warehouse, semantic model, Power BI
Incremental batch data pipeline for e-commerce order data, built with Airflow and Polars using a medallion (bronze/silver/gold) architecture, Pandera data quality gates, and dimensional modeling on DuckDB.
Building my first ETL pipelines using Python to learn more about data engineering. Learning by doing.
Production-inspired ETL pipeline built with Python and PostgreSQL featuring data validation, transformation, incremental loading, audit logging, reporting, archiving, CLI support, retry mechanism, and automated testing.
End-to-end ETL pipeline: incremental API ingestion, dbt transforms, Airflow orchestration, and CI
End-to-End Retail ETL Pipeline using Apache Airflow, PostgreSQL, Docker, and Python with Incremental Loading and Data Quality Validation.
Enterprise SQL data platform demonstrating metadata-driven ETL, staging, dimensional warehousing, incremental loading, auditability, and Docker/LocalDB deployment using SQL Server and SSDT.
Production-oriented Data Engineering pipeline with Python, DuckDB, Data Quality, incremental loading, automated testing, and dimensional modeling.
Real-time AWS ecommerce data pipeline using MSK Serverless, MSK Connect, S3, AWS Glue, Delta Lake, SCD Type 2, and Redshift Serverless.
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.
Azure Data Factory end-to-end project demonstrating real-world data engineering workflows including API ingestion, on-premises data migration, incremental loading with watermarking, pipeline orchestration, Logic App alerts, audit logging, and REST API pagination.
Azure SQL → Azure Data Factory → ADLS Gen2 → Databricks: watermark-based incremental loads, Auto Loader, Delta Live Tables with SCD Type 2. Guided project, extended.
End-to-end Azure data engineering solution for U.S. energy market analytics with ADF, Functions, ADLS Gen2, Synapse, Terraform, and Power BI.
Local-first NYC TLC lakehouse with Bronze/Silver/Gold layers, data quality, incremental loading, and Airflow backfills
Incremental data ingestion pipeline using Azure Data Factory, SQL Server and ADLS Gen2 with watermark-based loading and Parquet storage
End-to-end retail data engineering platform with incremental ETL, SCD Type 2, data quality, audit logging, MinIO, PostgreSQL, Apache Hop, Superset, and Docker.
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.
To associate your repository with the incremental-loading topic, visit your repo's landing page and select "manage topics."