H&R Block Tax Analytics & Automation

Overview

Project-based tax analytics and automation work focused on filing-pattern analysis, ETL restructuring, Power BI error-tracking dashboards, and Python automation for recurring reporting and backups. Presented as a resume project—not as an implied endorsement by H&R Block.

Business problem

Where do filing workflows bottleneck, which error patterns need attention, and how can ETL and reporting automation improve processing efficiency and leadership visibility?

Analysis and approach

• Analyze user filing patterns and process data for bottlenecks
• Restructure ETL pipelines to improve processing efficiency
• Create Power BI dashboards with automated data validation
• Build Python scripts for recurring reporting and backups

Skills: ETL restructuring · Process bottleneck analysis · Dashboard design · Reporting automation

Evidence and visuals

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  Evidence placeholder — add cleared Power BI screenshots when rights allow.

Findings and outcome

• Workflow bottlenecks become actionable when filing patterns and exception categories are analyzed together.
• ETL restructuring can reduce processing lag before dashboard work starts.
• Automated validation improves trust in leadership-facing activity and error views.

Outcome: Analyzed filing patterns and bottlenecks, restructured ETL pipelines for about 40% faster processing, and delivered Power BI dashboards with automated validation.

Recommendations

1. 1

   Keep exception categories and processing-time metrics on the same operating review cadence.

2. 2

   Document ETL source-of-truth rules after inconsistency fixes.

3. 3

   Restore or replace the public project page before relying on an external demo link.