Mobile Analytics
Reliability
Framework
An end-to-end mobile tracking audit and monitoring framework that fixed Firebase data quality, reduced a 34% revenue gap to 1.2%, and restored reliable product and sales reporting.
The dashboard
looked fine.
The data wasn't.
A high-volume mobile app running Firebase Analytics and BigQuery had accumulated tracking failures across revenue events, consent logic, funnel steps, and iOS / Android parity. Product, Sales, and DPO teams were seeing symptoms, but the issues were hidden behind dashboards that appeared clean.
A reusable audit flow from event collection to decision readiness.
Audit and taxonomy
Firebase DebugView sessions, BigQuery event-level analysis, standardized taxonomy, and parameter dictionary.
Tracking implementation
Commanders Act dispatch logic with GDPR consent gating, parameter validation, server-confirmed triggers, and consistent event structure across iOS and Android.
Validation and monitoring
BigQuery and dbt validation suite with daily checks for nulls, duplicates, naming compliance, platform parity, sequence integrity, and rolling anomaly detection.
Results that changed trust
in the data.
Based on event-level analysis in BigQuery and validation checks built for the framework.
Trust the data.
Turns tracking quality from reactive debugging into a repeatable analytics process.
I owned the end-to-end analytics reliability work: auditing Firebase events, defining the tracking taxonomy, rebuilding Commanders Act event dispatch with consent gating, writing BigQuery validation checks, and structuring the monitoring framework for long-term data quality.
Explore the rest of the work.
The repository includes the event audit log, approved taxonomy, measurement plans, Commanders Act tracking logic, SQL validation suite, anomaly detection, GDPR checks, and results summary.