Led a performance and data quality optimization initiative for Payrix’s enterprise dashboards. Partnered with data engineers to fix inconsistencies, improve validation, and optimize SQL queries into Python scripts. Streamlined AWS Athena and AWS Glue workflows, updated and standardized data dictionaries, and documented AWS QuickSight dashboards in detail, including data sources, calculation logic, and update schedules. Validated dashboard accuracy post-updates, ensuring faster, more reliable insights while maintaining compliance with privacy and security standards.
Project Overview
Challenges & Solutions
Challenges
Existing Payrix dashboards had slow load times and performance bottlenecks
Data inconsistencies caused by processing errors and incomplete validation
Redundant data pulls increasing system strain and reducing efficiency
Lack of structured documentation for AWS QuickSight dashboards and data definitions
Need to ensure all metrics were accurately calculated and aligned with governance standards
Requirement to validate data after backend script updates to maintain accuracy
Solutions
Partnered with data engineers to review and analyze datasets for accuracy and completeness
Identified and corrected data inconsistencies by improving validation logic in scripts
Rewrote inefficient SQL queries as optimized Python scripts to improve performance
Streamlined AWS Athena and AWS Glue workflows to eliminate redundant data pulls and reduce processing time
Created and updated comprehensive data dictionaries, standardizing metric definitions and ensuring alignment with business rules
Documented each AWS QuickSight dashboard, detailing the data shown, calculation logic, and update schedules
Validated AWS QuickSight dashboards after backend changes to ensure accuracy of metrics and visualizations
Worked with privacy and cybersecurity teams to ensure data flows complied with security and governance policies