10 Academy profile site
Data Engineering tools
Apache Kafka
Apache Airflow
Docker
Spark
ETL/ELT
DBT
Programming
Python
Java
SQL
React
Testing
Unit Test
Tools and Technologies
CI/CD, Docker
Travis CML, DVC, MLflow
SSAS,SSIS,SSRS.
AWS
Oracle, SQL Server, Dbeaver, Sql Developer.
Visualization Tools
Flask
Streamlit
Heroku
PySpark
Pandas
Power Bi, Tableau.
About me
Birhanu is a Junior Data Engineer with M.Sc. in Computer Science and Engineering. I am familiar with OOP, Python, SQL, ETL/ELT pipelines, building data visualizations, data analysis, and ML modeling. I've also built data pipelines using Apache frameworks such as Kafka, Airflow, and Spark.
Education
- Adama Science and Technology University (2021-2022)
M.Sc.(Computer science and Engineering)
Design and Analysis of Algorithms
Image Processing
Software Architecture and Design
Distributed Systems
Natural Language Preprocessing (NLP)
Advanced Machine Learning
Research Methodology and Ethics
Project Title: Fake Review Detection for Online Electronics Marketing using Hybrid Deep Neural Network Model
- Haramaya University (2016-2019)
B.Sc.(Information Technology)
Calculus I, C++, Computer Architecture and Organization.
Data and Network Communications, and Artificial intelligence.
Fundamental and Advanced Database
Data Structures and Algorithms.
Discrete Mathematics, Information Retrieval, and Security.
Internet Programming I-III, Java, Python.
Project Title: Student Information Management System
10 Academy (September 2022- November 2022)
Data Engineering, Machine Learning, and Web 3 Engineering
Worked on real-world challenges in the domains of Data Engineering, ML, and Web 3 Engineering
A three-month intensive training program filled with real-world challenges.
Projects - A/B hypothesis testing, Pharmaceutical Sales prediction, ALgorand blockchain, Natural Language Processing, Twitter Sentiment Analysis, and Causal Inference.
Work Experience
- Business Intelligence Specialist
Safaricom Telecommunications Ethiopia PLC (January 2023- Present)
- Designed and implemented SSAS cube from Oracle database, generating daily reports with DAX queries and KPIs for transaction and subscriber analysis, fostering data alignment between CBECORE and EDW BI team.
-Engineered cube, ETL workflows using SSIS, and dashboard for EBU team, facilitating monthly tracking of voice and data KPIs, revenue, ARPU, and prepaid/postpaid usage across multiple communication channels, enhancing operational efficiency.
Developed USSD HITs Report to evaluate customer performance and status through various KPIs associated with mobile USSD code engagement, enabling targeted interventions to optimize customer experiences and drive business growth.
- Summary Internship
OBN TV Station (March 2019 to June 2019)
Achievements
Develop Traffic management system that control the
program automatically. Building network infrastructure to handle data center
Projects
Build a data engineering pipeline that allows recording Amharic and Swahili speakers to read digital texts on in-app and web platforms. An end-to-end ETL data pipeline that uses Apache Kafka, Apache Spark, and Apache Airflow in order to receive user voice audio files, transform them, and load them into a data warehouse that will later be used for text-to-speech conversion machine learning projects.
Design and build a robust, reliable, large-scale trading data pipeline for both crypto and stock market trading that can run various backtests and store various useful artifacts in a robust data warehouse system. Users are prompted with several different stock and crypto trading options and parameters.
We created a trustworthy hypothesis-testing algorithm for an advertising agency to ascertain whether a recent advertising campaign significantly increased brand awareness. Users are chosen for a certain audience depending on the experiment they took part in and the yes/no answers they gave. A/B testing with machine learning, classic A/B testing, and sequential A/B testing were the three methods employed for analysis.
A pharmaceutical store wants to forecast sales in all their stores across several cities six weeks ahead of time. As an ML engineer, my focus was to build and serve an end-to-end product that delivers this prediction to analysts in the finance team. I also highlighted the important features like promo that impact the number of sales.