Engineering Deliverability: Building a Cold Email Verification Pipeline with Python

Cold email is a delivery problem,not a copy problem. Here’s how I built a Python-powered verification pipeline that increased my agency’s open rates from 3% to 42%.

The Pipeline Architecture:

1. Input: Raw lead list (CSV/API).
2. Stage 1 – Syntax & MX Check: Validate email format and domain MX records.
3. Stage 2 – API Validation: Use a service like NeverBounce or Hunter.
4. Stage 3 – DNS Record Audit: Check recipient domain’s SPF/DKIM/DMARC.
5. Output: Clean, verified list ready for sending.

Core Code Snippet (Python - Conceptual):

`import dns.resolver
import requests
import pandas as pd
from typing import Dict, List

class EmailVerificationPipeline:
def init(self, hunter_api_key: str):
self.hunter_api_key = hunter_api_key

def check_mx_record(self, domain: str) -> bool:

    """Check if domain has valid MX records."""

    try:

        answers = dns.resolver.resolve(domain, 'MX')

        return bool(answers)

    except:

        return False

def verify_via_hunter(self, email: str) -> Dict:

    """Use Hunter.io API for verification."""

    url = f"<https://api.hunter.io/v2/email-verifier?email={email}&amp;api_key={self.hunter_api_key}&gt;"

    response = requests.get(url).json()

    return response.get('data', {})

def audit_dns_security(self, domain: str) -> Dict:

    """Check for SPF, DKIM, and DMARC records."""

    records = {}

    for record_type in ['TXT', 'SPF', 'DKIM', '_dmarc']:

        try:

            answers = dns.resolver.resolve(f"{record_type}.{domain}" if record_type == '_dmarc' else domain, 'TXT')

            records[record_type] = [str(r) for r in answers]

        except:

            records[record_type] = None

    return records

def process_list(self, emails: List[str]) -> pd.DataFrame:

    """Main pipeline function."""

    results = []

    for email in emails:

        domain = email.split('@')[1]

        mx_valid = self.check_mx_record(domain)

        hunter_data = self.verify_via_hunter(email)

        dns_audit = self.audit_dns_security(domain)

    # Decision Logic
    deliverability_score = 0
    if mx_valid and hunter_data.get('status') == 'valid':
        deliverability_score = 85
    if dns_audit.get('_dmarc'):
        deliverability_score += 15

    results.append({
        'email': email,
        'mx_valid': mx_valid,
        'hunter_status': hunter_data.get('status'),
        'has_dmarc': bool(dns_audit.get('_dmarc')),
        'deliverability_score': deliverability_score
    })
return pd.DataFrame(results)



    

    

Usage

pipeline = EmailVerificationPipeline(hunter_api_key='YOUR_KEY')
df = pipeline.process_list(['[e-mail]', '[e-mail]'])
clean_list = df[df['deliverability_score'] >= 85]['email'].tolist()
`
This pipeline becomes the mandatory gatekeeper for all outbound. No verification, no send.

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