I will build your etl data pipeline using python sql airflow and AWS

Struggling with messy, scattered data that's hard to trust? I build reliable ETL pipelines that turn raw data into clean, query-ready information the way production data teams do it.

I work with Python, SQL, Apache Airflow, PySpark, and AWS to move your data from source to a structured warehouse, with validation along the way so bad data gets caught early.

What I can help with:

ETL/ELT pipeline design and development

Apache Airflow orchestration (scheduled DAGs)

Data ingestion from APIs, CSVs, and databases

Data transformation with Python/PySpark

Data warehouse modeling (fact/dimension tables, PostgreSQL)

Data quality validation and automated checks

I've built end-to-end pipelines handling 100,000+ records across multiple sources, structured into clean, analytics-ready warehouses.

I care about clean code and clear communication you'll know what's happening at every stage, not just get a black box at the end.

Got a different data source or need? Message me first happy to check the fit before you order.