You will get Data Cleaning, Validation & Deduplication for Your Contact List (CSV/Excel)
Rising Talent
Rising Talent
Project details
Send the list you already have (CSV or Excel) and get it back trustworthy: clean, validated, and deduplicated. Most business lists look fine and are quietly full of duplicates, dead emails, and broken phone numbers, and you only find out when an outreach campaign bounces.
You send the raw file: a CRM export, a list your team exported from a tool you use, or a hand-built spreadsheet. I normalize every field, validate email, phone, and url, and deduplicate on a key computed in code, including the same business entered under a name variant. You get back a clean CSV or Excel with a fixed column order, plus a brief that accounts for every record: delivered, deduplicated, or held back, each with the reason.
Why me: validity is decided by tested Python, not trusted by eye. The pipeline behind this carries 41 tests scored against a known answer, so what it catches is measured, not assumed.
How it works: you send the file, we agree the column order once, and I deliver the clean file, a method note, and the brief. The default cleaning runs fully offline.
You send the raw file: a CRM export, a list your team exported from a tool you use, or a hand-built spreadsheet. I normalize every field, validate email, phone, and url, and deduplicate on a key computed in code, including the same business entered under a name variant. You get back a clean CSV or Excel with a fixed column order, plus a brief that accounts for every record: delivered, deduplicated, or held back, each with the reason.
Why me: validity is decided by tested Python, not trusted by eye. The pipeline behind this carries 41 tests scored against a known answer, so what it catches is measured, not assumed.
How it works: you send the file, we agree the column order once, and I deliver the clean file, a method note, and the brief. The default cleaning runs fully offline.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$120
|
Standard
$300
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Frequently asked questions
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EC
Eric C.
Jul 6, 2026
Market Research - pricing
A real pleasure working with Vinicius. Clear, intelligent communication. Detail oriented and I can tell Veinicius takes pride in his work and wants to ensure you are happy with the results. The deliverables on this project exceeded my expectations both in turnaround time and format. I am already planning my next project with Vinicius.
BM
Brandon M.
Jul 5, 2026
Python Developer — Real Estate Market Intelligence Data Pipeline (Playwright + Apify + Airtable)
Vinicius was an absolute pleasure to work with. His communication was excellent every step of the way, and he made sure to communicate potential issues, and solutions before moving forward with any steps in order to ensure our project was built properly. Throughout the project he made several suggestions that not only made our project more efficient, but will help us in the long run as well. If you are looking for a developer, and want someone who will get exactly what you need and exceed every expectation then dont pass up on Vinicius. Also, he finished a project that many quoted weeks in less than a week, and didnt take any shortcuts. Incredible work. I look forward to bringing him more work, and will continue to use him for future projects!
PK
Paxley K.
Jul 3, 2026
Python Data Engineer for Data Cleansing
Vinicius delivered excellent work on a complex data-engineering project.
He built a reusable, profile-driven Python data cleansing framework with strong architecture, clear documentation, automated tests, and disciplined output controls. The project required careful handling of procurement, payment, supplier, government, and retail datasets, with strict boundaries around neutral data-quality signals and no fraud, legal, audit, or enforcement conclusions.
Vinicius understood the brief quickly, respected the architecture, communicated clearly, accepted detailed review feedback, and patched issues fast. He also added thoughtful improvements such as validator auto-discovery, captured-vs-authored guardrails, clean CLI behaviour, and a full pytest suite.
The final delivery was professional, well-structured, tested, and ready for the next productisation phase. I would confidently work with Vinicius again.
He built a reusable, profile-driven Python data cleansing framework with strong architecture, clear documentation, automated tests, and disciplined output controls. The project required careful handling of procurement, payment, supplier, government, and retail datasets, with strict boundaries around neutral data-quality signals and no fraud, legal, audit, or enforcement conclusions.
Vinicius understood the brief quickly, respected the architecture, communicated clearly, accepted detailed review feedback, and patched issues fast. He also added thoughtful improvements such as validator auto-discovery, captured-vs-authored guardrails, clean CLI behaviour, and a full pytest suite.
The final delivery was professional, well-structured, tested, and ready for the next productisation phase. I would confidently work with Vinicius again.
JL
Jordan L.
Jun 27, 2026
Excel Macro for Form Filling
He is great. His work was excellent. I will definitly be looking to him for future needs.
VC
Vanessa C.
Jun 11, 2026
Data Scraping for Physician List
Vinicius completed the work ahead of schedule and with accuracy. I would hire him again.
About Vinicius
AI Automation & Web Scraping Engineer | Python, LLMs, Data Extraction
100%
Job Success
Niteroi, Brazil - 11:58 pm local time
Background: Mechanical Engineering at PUC-Rio, which shaped a systems approach. I think in terms of inputs, transformations, validation, and observability, not ad-hoc scripts.
Recent public projects (portfolio below, code on GitHub):
- Record Refinery: raw business records in, a clean validated deduplicated dataset plus a QA brief out; email, phone, and URL validated in code
- SQL Agent: ask a database questions in plain English; the agent writes the SQL, runs it against a read-only connection, and fixes its own errors before answering
- MCP Listings: an MCP server on Anthropic's official SDK, exposing a dataset to Claude as five typed tools over stdio and Streamable HTTP
- RAG Quality: three retrievers scored on a labelled set (hit@k, MRR) so the choice is measured, plus grounded answers with citations that refuse when the documents don't support one
- Mini RAG, AI Watcher, BCB Data Pipeline: document Q&A with cited sources, feed monitoring with structured summaries, and a daily macroeconomic ETL refreshed by GitHub Actions
Strongest at:
- AI agents and LLM pipelines with Claude: tool use, forced schemas, self-correction loops, grounded answers
- MCP servers: connecting Claude to your database, CRM, or internal API
- Web scraping including anti-bot handling (curl_cffi, patchright stealth browser, Cloudflare-protected directories)
- Document extraction (PDFs, invoices, contracts via OCR + LLM structured output; see pdf-extract and doc-eval)
Also comfortable with: Python backends (FastAPI), SQL data work, REST API integrations, and email validation and lead-list cleaning pipelines (dedup and validation in code; see record-refinery).
My workflow includes AI coding assistants used deliberately. This is a methodology I have invested in, and it consistently ships cleaner systems faster than working alone.
I take on projects where clean, verifiable output and reliability matter more than the lowest bid. If that is what you are after, we will work well together. Open to small test scopes so you can validate quality before committing to anything larger.
Steps for completing your project
After purchasing the project, send requirements so Vinicius can start the project.
Delivery time starts when Vinicius receives requirements from you.
Vinicius works on your project following the steps below.
Revisions may occur after the delivery date.
Send file and columns
You send the raw CSV or Excel and we agree the exact fields and column order.
Normalize and validate
I shape every field to a consistent form and validate email, phone, and url.