Revenue Reconciliation & Data Quality System
The Challenge
SaaS companies lose 5-10% of monthly recurring revenue to billing errors; users with active system access who aren't being billed, or inactive users still being charged. Manual reconciliation is time-consuming (20-40 hours monthly), error-prone, and usually happens too late to recover lost revenue. Finance teams need automated systems to catch discrepancies before they compound.
What I Built
Automated reconciliation system that cross-references user access logs against billing records to identify revenue leakage. Built end-to-end solution combining SQL database design and Python ETL pipelines analyzing 500 users across 7 issue categories.
Validated Business Impact:
• $260,388 in annual revenue at risk identified across 171 billing discrepancies
• $3,292/month in quick wins recoverable within one week
• Automated 20-40 hours of monthly manual reconciliation work
• System pays for itself recovering just 2% of identified leakage
Technical Approach
Designed a normalized SQL database containing two core staging targets: user_accounts and billing_subscriptions. Built modular analytical scripts containing tailored window logic and cross-joins to map operational tracking issues seamlessly.
SELECT u.user_id, u.email, u.plan_type,
CASE u.plan_type
WHEN 'Starter' THEN 49
WHEN 'Pro' THEN 99
WHEN 'Enterprise' THEN 299
END as expected_monthly_revenue
FROM user_accounts u
LEFT JOIN billing_subscriptions b ON u.user_id = b.user_id
WHERE u.account_status = 'active'
AND u.plan_type != 'Free'
AND b.subscription_id IS NULL;
Python ETL pipeline handles transactional preprocessing and exports data directly into dynamic schema outputs.
- Overview Automated reconciliation system identifying $260K+ annual revenue leakage by matching user access against billing records. Categorizes 171 discrepancies across 7 issue types, quantifies financial impact, and provides prioritized action recommendations.
- Technologies Used
- Skills Demonstrated Revenue leakage analysis, SQL database design, complex SQL queries (CTEs, joins, window functions), Python ETL automation, financial impact quantification, Tableau dashboard development, SaaS business operations, prioritization frameworks
- Business Impact Identified $260K+ annual revenue at risk, automated 20-40 hours monthly manual work, created prioritized action framework enabling finance teams to focus on high-impact fixes, system ROI positive after recovering just 2% of identified leakage