Data Analytics at Scale
Business Intelligence Engineers (BIEs) specialize in working with large data sets while understanding and addressing business needs. This unique combination enables them to create solutions like interactive dashboards, models, and ad hoc analyses that maximize value and impact.
A top 10 Fortune 500 company faced challenges tracking Travel and Entertainment (T&E) costs for over 1.6 million employees. Despite multiple attempts by tech and business teams, end users lacked the insights needed to see the big picture or drill down into details. Teams often resorted to manually compiling reports, leading to inconsistent data and inefficiencies.
Tony built a cloud data lake that ingested and organized financial and spending data from multiple sources. Using Amazon QuickSight, he developed an interactive dashboard providing an end-to-end view of T&E expenses, from high-level summaries to individual employee details.
The dashboard gained over 500 users within two months of launch and received overwhelmingly positive feedback. As a result, a fully staffed tech team took over the project, rolling it out to thousands of users across the company.
While working with an FP&A team using IBM Cognos, Tony noticed inefficiencies in reconciling scenarios like YoY, QoQ, or Actuals vs. Plan. The existing system was slow, relied heavily on Excel, and required multiple data pulls to identify variances.
To address this, Tony developed a Python-based web application using the Dash framework hosted on AWS Fargate. The application allowed users to instantly pull and compare scenarios, with the ability to click through cost centers and accounts to pinpoint variance drivers.
This tool became the default platform for accessing financial data, significantly improving efficiency and accuracy.
An ETF provider with over $50B in AUM lacked internal tools to view historical data for its own or competitor products. Reporting on standard metrics like AUM growth or fund performance required manual data pulls, which were then compiled into PDFs or PowerPoint presentations.
To streamline this process, Tony designed a robust data architecture and developed an interactive application using R Shiny. The application enabled users to analyze the $7T ETF market, providing customizable views across sectors, regions, and individual ETFs.
This tool empowered users to explore millions of data combinations with just a few clicks, enhancing visibility into the ETF market and detailed insights into their own funds.