Financial data breaks in ways normal data does not. Tickers get renamed. Stocks split. APIs change their schema overnight or rate-limit you during market hours. A pipeline that worked yesterday silently ships wrong prices today. I have spent 5+ years handling exactly these failures, so my pipelines are built to detect them, not just crash on them.

What I build

• Apache Airflow pipelines with retries, alerts, and clear failure logs.
• AWS Glue and Lambda jobs for serverless, pay-per-run processing.
• Data warehouse setup on PostgreSQL or Snowflake, modeled for fast queries.
• Web scraping pipelines when no clean API exists for your data source.
• Scheduled automation: intraday updates during market hours, heavy jobs after close.

Pipelines I have shipped

AWS Glue ETL pipelines for financial and business data across multiple freelance clients. Data pipelines into warehouses using Python and Airflow at ML Sense Solutions, where data accuracy improved by 95%. A job analytics platform scraping millions of listings. An automated system that transcribed 5TB of video into text and loaded it into Google Sheets. The data layer behind Meyka AI, updating market data for major global exchanges daily.

Why it costs less with me

I have cut cloud costs by 50% on two separate teams by picking the right services and killing waste. A pipeline is not just code, it is a monthly bill, and I design for both.