End-to-End Product Data Scraping, Processing, and Structuring for E-commerce Upload

Project Overview:

I am looking to hire an experienced freelancer or team to perform large-scale data scraping, processing, and structuring of product data from multiple competitor websites.

The objective is to create a complete, clean, and standardized dataset, along with fully processed images, that can be directly uploaded to my e-commerce platform with minimal manual effort.

This project includes:

Deep scraping (beyond visible listings)
Data cleaning and structuring
SKU generation
Image downloading, renaming, watermarking, and format conversion
Organized cloud storage and delivery
Scope of Work
1. Data Scraping (Comprehensive Coverage)

You must extract all models and all associated spare parts, not just what is visible on listing pages.

A. Maxbhi.com

Target URLs:

https://www.maxbhi.com/searchmodel.html?brand_id=6268&ptid=0&query_v3=ipad
https://www.maxbhi.com/searchmodel.html?brand_id=6271&ptid=0&query_v3=tab

Critical Requirement:
Maxbhi brand pages show limited results (~60), but significantly more models exist via search. You must extract:

ALL models and their images
ALL spare parts for each model

Brands to Cover:
Nothing, Apple, Samsung, OnePlus, Oppo, Vivo, Realme, Pixel, Asus, Honor, Infinix, iQOO, Lenovo, LG, Xiaomi, Tecno

B. Cellspare

Extract:

ALL models
ALL spare parts
Model-level images

Brands to Cover:
Nothing, Apple (including iPads and Apple Watches), Samsung, OnePlus, Oppo, Vivo, Realme, Pixel, Asus, Honor, Infinix, iQOO, Lenovo, LG, Xiaomi, Tecno, Poco

C. Injuredgadgets.com

Source: https://www.injuredgadgets.com/

Extract:

All Apple Watches
All iPads
All spare parts associated with these devices
2. Data Extraction Requirements

For each product, extract:

Product Name
Color (if available)
Model Name
Category (e.g., display, battery, etc.)
Description
Actual Price
Markup Price / MRP
Discount (if available)
Brand Name

Ensure clean, complete, and consistent data.

3. Data Structuring (CSV Output)

Deliver a well-structured CSV file where:

Each row = one product
Each column = one attribute

Mandatory Columns:

Product Name
Color
Model Name
Category
Description
Markup Price
Actual Price
Brand Name
img_1
img_2
img_3
img_4
img_5
img_6
4. SKU Generation
Generate a unique SKU for each product
Maintain a consistent, scalable format
5. Image Extraction and Processing
A. Image Collection
Download all product images (not just URLs)
B. Naming Convention (Strict)

Format:
ProductName_Color_SAHII_SKU_Img1.webp

Example:
Ringer_iPhone14ProMax_Black_SAHII_12345_Img1.webp

Requirements:

Include Product Name + Color + SAHII + SKU + Image Number
Remove competitor references from names
C. Multiple Images Handling
Map images into CSV columns (img_1 to img_6)
Each column must contain the correct file name
D. Image Processing
Convert all images to WebP
Apply watermark “sahii.in” (provided PNG)
Maintain quality with compression
6. Storage & Organization
Upload all images to a Mega (mega.nz) folder
Maintain clean folder structure
Ensure exact mapping between CSV and images
7. Final Deliverables
A. CSV File
Clean, structured, complete dataset
One row per product
All required columns filled
Correct image mapping
B. Image Dataset
All images downloaded, renamed, watermarked, converted to WebP
Uploaded to Mega
C. Data Integrity
No duplicate entries
No missing mappings between products and images
As little competitor branding in output images as possible
Complete coverage of ALL spare parts for each model
Key Expectations
Exhaustive scraping (not surface-level)
Ability to bypass pagination and search limitations
High accuracy and attention to detail
Output must be ready for direct e-commerce upload