Custom Python Data Pipeline for B2B Leads
Amitabh Mohan Verma
Custom Python Data Pipeline: Extracting & Enriching 2,000+ Verified B2B Leads
The Challenge (The Problem)
Standard B2B databases like Apollo and ZoomInfo are heavily over-farmed, leading to low response rates, dead emails, and outdated phone numbers. A client needed a fresh, highly targeted list of 2,000 verified manufacturing and local business leads in specific US metro areas, complete with direct owner contact details and decision-maker names that mainstream databases miss entirely.
The Custom Solution (How You Built It)
I designed and executed a custom Python automation pipeline tailored specifically for the target directory's architecture:
Stealth Extraction: Built an asynchronous scraping engine utilizing Playwright and custom proxy rotation to bypass rate limits and pull raw records cleanly.
Multi-Step Enrichment: Programmatically cross-referenced company domains against public leadership pages and targeted X-Ray queries to isolate direct decision-maker names (Founders, VPs, Managing Directors).
Automated Packaging: Built a custom export script using pandas and openpyxl to output a clean, mobile-optimized Excel spreadsheet complete with status badges, custom formatting, and clickable hyperlinks.
The Results & Deliverable (The Proof)
100% Active Data: Zero disconnected phone numbers or stale listings since data was pulled live at execution.
Client-Ready Format: Delivered a structured workbook ready for immediate cold outreach campaigns without requiring manual data cleanup.
Built-In Suppression: Incorporated a local SQLite database ledger to ensure zero duplicate records are ever shipped across future recurring orders.
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Posted Oct 31, 2026
Built custom Python scraping pipeline to extract and enrich 2k+ verified B2B leads with decision-maker emails, direct phones, and clean Excel formatting.
