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Abrha Gebrehiwet

Abrha Gebrehiwet

Data Analyst Intern en Prompt BI

Ciencias de la Computación en Mekelle University

Etiopía

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¡Hola, soy Abrha Gebrehiwet!

Data Analyst Intern en Prompt BI

Results-driven Data Analyst with expertise in SQL, Python, Excel, and Power BI, specialized in solving climate and sustainability challenges through data-driven insights. Proficient in end-to-end analytics—including data wrangling, exploration, visualization, and machine learning—with focused applications in carbon accounting, ESG metrics, and climate risk analysis. Committed to leveraging data science to help organizations achieve net-zero targets and strengthen climate resilience amid evolving regulations.

Redes sociales

Experiencia

Educación

Certificaciones y Distintivos

No se agregó certificaciones o distintivos

Proyectos

Multi-Vendor E-commerce Website

https://github.com/Abricos-gt/Multi-Vendor-Ecommerce-Website.git

Backend Developer(Python)

A multi‑vendor ecommerce platform where multiple vendors can register, list and sell their products, while customers browse, cart, and checkout. Admins manage vendors, catalog, orders, refunds, and monitor performance with built‑in analytics.

Frontend: Vue 2/3 SPA (in src/), charting via vue-chartjs.
Backend: Flask + SQLAlchemy (in backend/).
Payments: Chapa checkout with webhook/callback verification.
Dashboards: Admin analytics (orders summary, sales over time, top categories), vendor management, refunds.
✨ Features
Multi-vendor cart → single or per‑vendor order creation
Chapa payment initialization, callback verification, and optional return URL suppression
Orders lifecycle: pending → paid/failed → completed/cancelled
Admin dashboard: counters, charts, recent orders
Vendor applications review and product management
Refund requests and processing

data analyst

Customer Behavior Analysis — Python, SQL, Power BI Nov 2025 – Dec 2025
– Analyzed behavior of 7,043 telecom customers to identify churn patterns based on tenure, contract type, and
monthly charges.
– Engineered new features including contract type buckets and tenure bands to improve segmentation and insights.
– Generated 11 visualizations (histograms, box plots, heatmaps) to highlight churn-prone segments.
– Revealed that 38% of churned users were on monthly contracts with less than 3-month tenure—suggested early
engagement strategies

Idiomas

Árabe

Profesional

Inglés

Profesional

Amárico

Nativo

Habilidades

Data Analytics

Machine Learning

Python

Microsoft Power BI

Microsoft SQL

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