Your data is spread across spreadsheets, exports, an old database and a few APIs, and every report starts with copy-paste and clean-up?

I build a Python ETL pipeline that collects it, cleans it, checks every row and loads it into your database. You can run it again anytime, with one command or on a schedule.

This is for you if • You re-clean the same exports again and again • Your data lives in several places and never quite matches • You're moving to a new database and can't afford a messy migration • You need clean, trusted tables before building reports or dashboards

What you receive • A working pipeline, tested on your real sample data • Clean tables: fixed dates and text, no duplicates, every row validated • A reject report: rows that break a rule are set aside with the exact reason, never guessed • Safe loading: a failed run rolls back, so your tables are never half-loaded • Full source code and configuration, Docker included, yours to keep • A short guide: how to run it, schedule it and change it

Options you can add • Sources: CSV, Excel, JSON, XML, Parquet, PostgreSQL, MySQL, SQLite, REST and GraphQL APIs, SFTP / FTP • Destinations: PostgreSQL, MySQL or SQLite, or clean CSV, Excel, JSON or Parquet files, an SFTP folder or your own API • Loading: full refresh, append, upsert (insert new and update changed rows) or incremental (only what changed) • Privacy: mask, hash or encrypt personal data before it is stored • Automation: scheduled runs, resume after a failure, alerts by e-mail, Slack or webhook, and a small dashboard with run history • One-time jobs: a database migration (for example MySQL to PostgreSQL) or merging years of spreadsheets into one table

You share a small sample of each source and where the data should go.

I send a written plan: tables, cleaning rules, and what's in and out. Nothing starts until you approve it.

I build and test the pipeline on your sample, with regular progress updates.

You receive the code, the guide and a first run report.

We review it together and I make your changes.

Proof you can check Built on the same rules as my open-source ETL engine, IronFlow: 1,443 automated tests, 91.5% branch coverage, transactional loads, and a reject file for every bad row. https://github.com/mojtaba-py-code/ironflow

Not included Hosting, database or paid third-party API fees (billed to you directly by those providers), and websites that need a login or block automated access.

Ready to start? Send me a message with a short description of your data, and I'll reply with a plan before you order.