incremental-loading

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

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

Building my first ETL pipelines using Python to learn more about data engineering. Learning by doing.

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-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

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.

Real-time AWS ecommerce data pipeline using MSK Serverless, MSK Connect, S3, AWS Glue, Delta Lake, SCD Type 2, and Redshift Serverless.

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.

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.

Midterm Data Pipeline using Python, PySpark, MongoDB, Data Quality, Upsert and Incremental Loading.

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.

Local-first NYC TLC lakehouse with Bronze/Silver/Gold layers, data quality, incremental loading, and Airflow backfills

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

An end-to-end retail data ETL pipeline that extracts CSV data, transforms and validates it with Pandas, and incrementally loads it into PostgreSQL. Includes automated testing, structured logging, idempotent processing, CPU-optimized loading, and HashiCorp Vault for secure credential management.

Real-time AWS ecommerce data pipeline using MSK Serverless, MSK Connect, S3, AWS Glue, Delta Lake, SCD Type 2, and Redshift Serverless.

End-to-end Azure data engineering solution for U.S. energy market analytics with ADF, Functions, ADLS Gen2, Synapse, Terraform, and Power BI.