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$
USD
I will automate invoice and purchase order reconciliation with python
Data Automation, ETL and Reconciliation
I help businesses reduce repetitive data work through Trubnikova Data Automation Studio. Services include CSV/Excel processing, ETL pipelines, invoice and purchase order reconciliation, data migration...
About this Gig
Turn invoice and purchase order exports into a repeatable reconciliation workflow.
I will build a Python pilot for one invoice CSV format and one PO CSV format, up to 10,000 rows each and 10 agreed validation or matching rules. Checks can cover missing PO references, duplicates, and quantity, price, vendor or currency differences.
You receive:
- Matched results and a traceable exception report
- Reusable source code with setup instructions
- Tests and agreed acceptance examples
- A short runbook and dependency/license list
- Two revisions within the agreed scope
My portfolio includes an independent synthetic-data demo with 59 passing Windows tests. It is a capability sample, not a client case study or a guarantee for every dataset.
This package works with structured exports. OCR, live APIs, ERP posting, payment authorization, tax decisions and bookkeeping or audit opinions are excluded. Pre-existing reusable components and dependencies are identified in the handover.
Please message me with anonymized sample files and your matching rules before ordering so we can confirm fit.
Technology:
Python
Expertise:
Data manipulation
•
Data validation
•
Normalization
My Portfolio
FAQ
Can you work with scanned invoices or live ERP systems?
This package covers two agreed CSV export formats. Scanned documents, OCR, live APIs and ERP posting require separate scoping and are not included.
What will count as an accepted delivery?
We agree sample inputs, matching rules and expected outputs before work begins. Acceptance covers those examples, repeatable execution, traceable exceptions, source code, tests and setup instructions.
Are the portfolio results from paid client projects?
The portfolio examples are independent demos built with synthetic data. Test counts refer to those fixtures. They do not guarantee identical performance or outcomes on every production dataset.
What do revisions include?
Two revisions cover corrections or adjustments within the agreed formats and rules. New formats, integrations or additional workflows are a scope change and need a separate quote.
