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. [login to view URL] Target URLs: [login to view URL] [login to view URL] 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. [login to view URL] Source: [login to view URL] 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: [login to view URL] Example: [login to view URL] 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 “[login to view URL]” (provided PNG) Maintain quality with compression 6. Storage & Organization Upload all images to a Mega ([login to view URL]) 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

Projektin tunnus (ID): 40317449