You will get Turn your PDFs or spreadsheets into a structured, queryable database
Project details
I turn PDFs, spreadsheets and CSVs into a relational database you can query, along with the code that produced it, so you can re-run it when new files arrive.
Most of the work is in what doesn't line up: merged cells, footnotes that change the meaning of a row, the same entity named three different ways across sources. I normalize that, deduplicate, and validate the output against the originals.
I've done this with government PDFs from three operators, each with a different layout, parsed by coordinate position rather than plain text and modeled into a single PostgreSQL schema. It's public and running: github.com/chipap-dev/lanchas
Most of the work is in what doesn't line up: merged cells, footnotes that change the meaning of a row, the same entity named three different ways across sources. I normalize that, deduplicate, and validate the output against the originals.
I've done this with government PDFs from three operators, each with a different layout, parsed by coordinate position rather than plain text and modeled into a single PostgreSQL schema. It's public and running: github.com/chipap-dev/lanchas
Database Type
MySQL, MS SQL, SQLite, PostgreSQLWhat's included
| Service Tiers |
Starter
$180
|
Standard
$400
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 1 | 2 | 3 |
Number of Tables Added | 5 | 10 | 15 |
Schema Diagram | - | ||
Permissions Setup | - | - | - |
Import/Export Data | |||
Admin Panel Setup | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$50
Additional Table Added
(+ 1 Day)
+$30
Schema Diagram
(+ 2 Days)
+$60
Admin Panel Setup
(+ 3 Days)
+$150Frequently asked questions
About Claudia
Data & Backend Engineer | Python, SQL, Django | ETL & API Integration
Tigre, Argentina - 6:13 am local time
At the club, the data comes from REST and SOAP APIs, SQL Server over ODBC, spreadsheets and scraping, all unified into a PostgreSQL model I designed.
What I work with:
• ETL/ELT pipelines: incremental ingestion with a per-source cursor, deduplication by business key, validation and consistency checks
• Data modeling: star schemas in dbt on BigQuery, dimensional modeling, PostgreSQL, SQL Server
• Orchestration: Airflow (CeleryExecutor, Docker Compose), 100+ automated processes with run history and alerts
• Integration: REST, SOAP, ODBC, PDF parsing with pdfplumber, web scraping
• Backend: Django, custom permission layers, reporting systems
• Testing: pytest and dbt tests in GitHub Actions
Two data systems of my own run in production and are public on GitHub. One parses government PDFs from three boat operators and models them as a star schema with 31 dbt tests. The other loads two public APIs into BigQuery append-only, so re-runs never duplicate. You can see them at chipap.net and github/chipap-dev.
I also migrated three separate servers into one database: I defined the matching rules and handed back the unresolved cases grouped by confidence level, so each area could decide the criteria.
Spanish native, English B2. Buenos Aires, Argentina (UTC-3).
Steps for completing your project
After purchasing the project, send requirements so Claudia can start the project.
Delivery time starts when Claudia receives requirements from you.
Claudia works on your project following the steps below.
Revisions may occur after the delivery date.
Review your files and confirm scope
I go through the samples you send, check what can be parsed reliably and what can't, and confirm the schema, the timeline and anything that falls outside the tier before I start.
Build the parser
I write the extraction logic per source, handling merged cells, footnotes and inconsistent formats, and check the output against the original files.