Case studies
Lead generation automation platform
A data pipeline for a services company that collects, normalizes, validates, and schedules outbound lead data so campaigns start without manual clean-up.
Case study updated:
PythonDjangoPostgreSQLautomation workflowsintegrationsschedulingmonitoring
Client and context
A services business needed to increase lead throughput without scaling operations headcount proportionally.
Business problem
Lead data came from multiple sources, had inconsistent quality, and required manual handling before campaigns could start.
Approach and solution
I designed an automation platform connecting data collection, normalization, validation, and campaign launch through explicit business rules.
Delivery scope
- Multi-source scraping with quality controls and deduplication.
- Central PostgreSQL data model with normalization rules.
- Campaign scheduler with step-level operational logging.
- Monitoring and recovery procedures for critical workflow failures.
Business impact
- Significantly shorter time from data acquisition to campaign launch.
- Higher lead quality and less manual data cleanup work.
- Scalable process without linear operational cost growth.