Revenue & Parking Tax Reporting Data Pipelines

Context

• Parking revenue generated across multiple garages and booking channels required consolidated reporting
• Tax filings depended on accurate aggregation of revenue from RCE systems, Square, and third-party reservation platforms
• Manual reconciliation created risk for reporting errors and delayed monthly close

Constraints

• Each revenue source exposed different data models, identifiers, and reporting granularity
• Reports needed to be grouped by location and legal entity for tax compliance
• Financial data required strong validation and reproducibility for audits

Solution

• Built Python-based data pipelines to extract and normalize revenue data from RCE, Square, and online reservation platforms
• Mapped all transactions to standardized garage- and entity-level schemas
• Automated generation of monthly revenue and parking tax reports with consistent output formats

Key Decisions

• Designed idempotent data pulls to safely re-run reports without duplicating revenue
• Separated data extraction, normalization, and reporting layers to simplify debugging and future expansion
• Kept each data source pipeline isolated to prevent failures in one system from impacting others

Impact

• Reduced monthly tax and revenue reporting preparation from multiple days to a repeatable automated workflow
• Improved reporting accuracy and confidence during internal reviews and external accounting checks

Tech