Work Experience 

• Mid-Level Data Engineer ((Apr 2022 - Current):

Adludio - Contract

At Adludio, I played a pivotal role in harnessing the power of big data, processing over 10 terabytes of historical information to fuel various machine-learning models and analytics. Delving into the vast digital landscape, I spearheaded the scraping of content from over 20 million sites/publishers, a move that greatly enriched and amplified the potency of our historical data. My expertise in AWS was exemplified as I developed intricate data marts utilizing AWS Athena, Glue-crawler, Spark, and other AWS managed services. This facilitated the construction of robust warehouses, databases, and data lakes on platforms like AWS S3 Bucket, RedShift, and Snowflake, ensuring rapid and reliable data accessibility. Experimenting with data models, such as Kimball, Inmon, and Datavault architectures, I yielded an agile and resilient data model tailored for the company's operations. Additionally, I pioneered the deployment of diverse pipelines for projects encompassing analytics, machine learning, and other ETL processes. My contributions also extended to the development of analytics and reporting dashboards, leveraging React, FastAPI, and Postgres.

My architectural prowess was evident as I orchestrated an end-to-end AWS data pipeline and data warehouse, integrating components like Glue, Athena, Spark, S3, Lambda functions, and EMR clusters. My engineering initiatives included crafting a data warehouse with Hive, managing Hive tables, and devising Spark code in Python for expedited data testing and processing. By modeling, aggregating, and triangulating multiple log datasets, I ensured they were seamlessly populated to RDS databases and established a dependable data warehouse on both AWS and Snowflake. These strategic projects not only streamlined various pricing models but also significantly enhanced the efficacy of campaigns. This optimization directly bolstered key performance indicators, including cost-per-engagement and cost-per-impression, culminating in substantial financial savings for the company by better targeting effective targets.

Key points:

• Managed over 10 terabytes of data, enhancing machine learning capabilities.

• Scraped content from millions of sites to enrich historical data.

• Deployed AWS tools for robust data storage and retrieval systems.

• Pioneered data structures tailored to the company using different models.

• Established efficient pipelines for analytics and machine learning.

• Developed reporting tools with React, FastAPI, and Postgres.

• Orchestrated a comprehensive AWS data pipeline and data warehouse.

• Optimized pricing models, enhancing campaign effectiveness and financial savings.

• Machine Learning Engineer (Dec 2021 - Apr 2022):

Ethiopian Public Health Institute (EPHI) - Paid Internship

At the Ethiopian Public Health Institute, I took on the challenge of harnessing advanced Machine Learning (ML) algorithms to drive transformative changes in Demography and Epidemiology research. My role involved the integration and analysis of data from the Ethiopian Demographic and Health Survey across multiple years, focusing on the complexities of under-five mortality rates. Leveraging the power of three distinct ML models, I discovered that Gradient Boosting (XGBoost) stood out in its predictive accuracy, offering a modern alternative to conventional Logistic Regression methodologies.

Diving deeper into the data, I identified a myriad of influential factors affecting Under-five mortality, spanning individual attributes like breastfeeding practices to broader environmental metrics such as aridity and vegetation indices. Recognizing the nuances of data interpretation, I ensured that correlations were meticulously vetted for causality. To achieve this, I employed the innovative NOTEARS algorithm, which facilitated the extraction of clear causal relationships among diverse features.

These analyses revealed critical intervention points. For instance, community dynamics, such as the number of children in households and water sanitation practices, combined with individual factors like birth intervals, emerged as direct contributors to under-five mortalities. Drawing on these insights, I championed a holistic, evidence-driven approach to health interventions. I proposed and advocated for a multi-pronged strategy that emphasized nutrition, family planning, and infrastructure improvements in water and sanitation. My contributions not only set a new standard for data-informed health strategies but also laid the groundwork for sustainable, impactful health initiatives aimed at reducing under-five mortality rates in Ethiopia.

Key points:

• • • Utilized advanced Machine Learning to innovate in Demography and Epidemiology research.

    • Analyzed multi-year data to focus on under-five mortality rates, endorsing XGBoost for its accuracy.

    • Employed the NOTEARS algorithm to extract clear causal relationships in data.

    • Identified community and individual factors as pivotal contributors to under-five mortalities.

    • Advocated for a comprehensive, evidence-based approach to health interventions.

    • Emphasized the importance of nutrition, family planning, and water sanitation in health strategies.

    • Set a precedent for data-driven health strategies in Ethiopia.

    • Laid foundational groundwork for sustainable health initiatives targeting under-five mortality reduction.

• Software developer (Mar 2021 - Oct 2021):

CNET 

At CNET, I seamlessly blended technological innovation with user-centric designs, driving the company's mobile application portfolio to new heights. My relentless pursuit of excellence, adaptability, and an innate aptitude for debugging enabled me to consistently deliver solutions that not only resonated with our users but also drove significant business value. By harnessing the power of Dart and Flutter, I was at the forefront of pioneering CNET's transition into cutting-edge mobile solutions, ensuring the apps remained relevant, efficient, and impactful in a rapidly evolving digital landscape.

Key contributions include:

• Orchestrating the integration of diverse payment gateways, streamlining e-commerce functionalities.

• Pioneering the integration of popular social services, enhancing user engagement and connectivity.

• Implementing real-time push notifications, elevating user interaction and app retention rates.

• Leveraging Flutter's cross-platform capabilities, ensuring a consistent and high-quality user experience across all devices.

• Exemplary debugging skills, ensuring seamless app performance and swift resolution of any issues.

• Software-developer (Aug, 2019 - Mar, 2021):

Addis Zeybe - Intern/ Part-time

At Addis Zeybe, I started as a Web Developer and built the company's main website. I added tools to help the site show up in search results and used Google Analytics to see how people used our site. Later, I moved to a Software Tester role where I checked another website to make sure it worked well and was easy for users.

Key points:

• Built and set up Addis Zeybe's main website.

• Added tools to improve the website's search ranking.

• Used Google Analytics to track website use.

• Tested another website to ensure it worked properly.

• Computer Engineer (Mar 2018- July 2018):

ICOG-Labs - Intern

At ICOG-Labs, I embarked on an enriching internship journey as a Computer Engineer. During my tenure, I took on the challenge of revamping the company's official website, ensuring it reflected the latest updates and resonated with the brand's identity. Venturing further into the realms of Machine Learning, I had the privilege to overhaul one of their flagship products focused on image classification. I employed both simple linear regression and the more advanced convolutional neural network (CNN) methodologies for image classification, enhancing the product's efficiency and accuracy.

Key points:

• Successfully updated and modernized ICOG-Labs' official website.

• Redesigned a pivotal machine learning product specializing in image classification.

• Utilized simple linear regression for foundational image classification.

• Integrated convolutional neural network (CNN) techniques to advance the product's classification capabilities..