Raw file in
Carrier rows with varying fields, duplicates, missing contact data, and business rules that must be applied consistently.
- Column-name normalization
- Deduplication by USDOT number
- Rule-based inclusion and exclusion
CSV / Excel cleanup for operations teams
Clean lead lists and CRM-ready CSVs without another heavy tool.
I turn messy exports into deduplicated, filtered, campaign-ready files, and when the work repeats, a lightweight Python workflow your team can run again.
Start with a sample file, not the full dataset. Fixed scope before full delivery.
- Best for
- CRM imports, lead lists, recurring reports
- Output
- Clean CSVs, README, optional script
- Boundary
- Client-provided files only
The practical problem
Your team knows the rules. The spreadsheet still eats the day.
Duplicates, inconsistent columns, missing values, regional filters, segment splits, and upload-ready CRM formats are rarely strategic work. They are small operational bottlenecks that compound every week.
Case study
FMCSA lead filtering demo
Built from a real freelance-style requirement: turn a raw carrier CSV into scored lead lists for campaign testing. This is a demo, not a claim that the original Upwork client hired me.
Clean files out
Separate campaign CSVs, a combined master file, review exports, lead scores, and simple notes for non-technical review.
- CRM-ready CSV exports
- A/B lead tiers and campaign codes
- README and repeatable workflow
Example output
GA small carriers export| Campaign | Company | City | Units | Drivers | Score | Tier |
|---|---|---|---|---|---|---|
| GA-SMALL-01 | Peach State Small Carrier LLC | Atlanta | 4 | 3 | 39 | A Tier |
| GA-SMALL-01 | Small GA Express | Augusta | 10 | 9 | 39 | A Tier |
| GA-SMALL-01 | Old Georgia Carrier LLC | Savannah | 8 | 5 | 28 | A Tier |
Second proof
Spreadsheet report automation demo
A weekly sales export with duplicate rows, inconsistent channels, test orders, and mixed statuses is converted into clean CSVs, a review queue, and an Excel dashboard with repeatable automation.
- Raw rows
- 60
- Clean rows
- 52
- Review rows
- 8
- Clean paid-order output
- Exception queue for pending, cancelled, refunded, and test rows
- Dashboard workbook with weekly and channel revenue charts
Services
Small data operations work with clear boundaries.
One-time cleanup
Remove duplicates, normalize columns, filter rows, and return clean CSV or Excel output.
Lead-list preparation
Split lead exports into campaign files, apply segment rules, score rows, and prep for CRM import.
Reusable automation
Create a lightweight Python workflow with a README so repeated cleanup can run again.
Process
A small sample is enough to scope the work.
Send a sample file and the rules in plain English. I will confirm fit, identify risks, and suggest a fixed scope before touching the full file.
-
1
Share a sample
A few rows are enough to inspect columns, duplicates, and output shape.
-
2
Define the rules
Which rows stay, which rows go, how duplicates are identified, and what files you need.
-
3
Receive clean outputs
Clean CSVs first; reusable script and README when the workflow repeats.
Good fit
Send this kind of work.
- CRM import cleanup
- Sales or marketing lead lists
- Shopify, event, directory, or report exports
- Recurring spreadsheet steps your team repeats
Not a fit
These stay out of scope.
- Scraping behind logins, CAPTCHAs, or paywalls
- Guaranteed leads, sales, or deliverability claims
- Bulk spam lists or private data collection
- Enterprise systems disguised as tiny cleanup tasks
Start small
Have a messy CSV waiting before a CRM upload?
Send a sample and the rules. I will tell you whether it is a simple cleanup, a campaign-prep task, or a reusable automation workflow. For clear small jobs, I can usually return a fixed quote within 24 hours.
Email the sample brief
Order through Fiverr
What to include
- Small sample file
- Duplicate rule
- Rows to keep or remove
- Output files needed