Nathanael Paat
Sales & Performance Analyst | Power BI, SQL, Looker Studio
Analyzed a taxi trip dataset using Python to uncover how time-based factors — month, day of week, and hour — along with payment method, influence tip earnings. Cleaned and prepared the raw data, then explored and analyzed patterns through visualizations to identify key trends. The analysis found that drivers earn the most tips and orders during 6 AM–6 PM shifts, while early morning (4–6 AM) and late evening (8–10 PM) shifts yield higher tips per trip, giving drivers a data-backed way to plan their schedules
0
56
Integrated SE Ranking data into Looker Studio to build a clean, client-ready SEO dashboard tracking keyword rankings, organic traffic, website audit results, domain performance, and backlink profiles. Structured and validated the raw data to ensure accuracy and consistency before visualization. The dashboard gave the client an at-a-glance view of SEO performance, making it easier to track progress and report results to stakeholders
0
63
Built for a bank's Consumer Lending Portfolio, this dashboard uses synthetic data (modeled on the original structure and metrics) to protect client confidentiality. The interactive Power BI dashboard covers national/regional performance, product breakdowns, mortgage quality, and vintage analysis. Data was cleaned in Excel, then structured with DAX measures, slicers, and bookmark navigation for a clean, professional multi-page experience, giving executives and the lending team clear insight into portfolio health and supporting faster, data-driven decisions.
0
88
Analyzed medical tray loan distribution and utilization from warehouse to client locations, then built an interactive Power BI dashboard to uncover key insights and improve operational decisions for a client I worked with through Upwork
0
112