Ekemini Otu

Data Engineer & Data Scientist with a strong foundation 

in Python, web scraping and data extraction — building 

production-grade pipelines and machine learning systems 

that take data from raw files all the way to executive 

dashboards and predictive models.

I design and build end-to-end data infrastructure that 

businesses can actually depend on — automated, tested, 

monitored and documented.

What I build:

→ Batch ETL pipelines using Apache Airflow, dbt and 

 Snowflake on AWS — with data quality tests, layered 

 transformation models and real-time alerting

→ Real-time streaming pipelines using Apache Kafka and 

 Apache Spark — processing live events continuously 

 into Snowflake

→ Machine Learning prediction models — classification, 

 regression and Random Forest — deployed as interactive 

 web apps using Streamlit

→ Analytics dashboards using Power BI connected 

 directly to Snowflake mart tables

→ Cloud infrastructure on AWS — EC2, S3, SSM, SNS, IAM

→ Containerised deployments using Docker and 

 Docker Compose

Recent Data Engineering projects:

Netflix ETL Pipeline — Automated batch pipeline that 

ingests 87,000+ rows of data daily from AWS S3, 

transforms it through 6 layered dbt models in 

Snowflake, and delivers Slack and email alerts on 

success or failure. Fully containerised with Docker 

on AWS EC2.

github.com/EkeminiImeOtu/netflix-etl-pipeline

Real-Time Streaming Pipeline — Live event processing 

system using Apache Kafka and PySpark Structured 

Streaming. Generates and processes Netflix viewing 

events in real-time with 15-second micro-batches 

written directly into Snowflake.

github.com/EkeminiImeOtu/netflix-streaming-pipeline

Olist E-Commerce Analytics — End-to-end analytics 

platform built on 1.5 million rows of real e-commerce 

data. Python ingestion from S3, 11 dbt models across 

staging and mart layers, Power BI executive dashboard 

showing revenue trends, top categories, delivery 

performance and payment breakdown.

github.com/EkeminiImeOtu/olist-ecommerce-pipeline

Machine Learning projects (AI Saturdays Abeokuta — 

part of the global AI6 movement):

Diabetes Prediction Web App — ML classification model 

deployed as an interactive Streamlit web application 

for early diabetes risk assessment.

github.com/EkeminiImeOtu/diabetes-web-app-prediction-using-Machine-Learning

Multiple Disease Prediction System — Predicts multiple 

diseases from patient data using classification 

algorithms.

github.com/EkeminiImeOtu/multiple_disease_prediction

Hotel Bookings Cancellation Prediction — Predicts 

cancellation likelihood to help hotels optimise revenue.

github.com/EkeminiImeOtu/hotel-bookings-cancellation-prediction

Flight Price Prediction — Forecasts flight prices using 

multiple machine learning algorithms.

github.com/EkeminiImeOtu/flight-prediction-using-machine-learning-algorithms

Flowers Classification — Image classification system 

using Random Forest.

github.com/EkeminiImeOtu/Flowers-classification-using-random-forest

My background in Python automation, web scraping, API 

integrations and machine learning means I can handle 

the full data journey — from collecting raw data, to 

engineering reliable pipelines, to building predictive 

models, all the way to delivering clean, actionable 

insights.

I also teach programming (Python, Java, Scratch, MIT 

App Inventor) to kids, teens and adults, and publish 

tutorials on YouTube — which means I communicate 

complex technical concepts clearly to both technical 

and non-technical stakeholders.

Tools: Python • Apache Airflow • dbt • Snowflake • 

Apache Kafka • Apache Spark • AWS • Power BI • Docker • 

SQL • Pandas • NumPy • Scikit-learn • Streamlit • 

Random Forest • Boto3

Certifications:

ALX Africa — Data Analytics (6-Month Programme)

ALX Africa — Python Programming (8-Week Training)

ALX Africa — Professional Foundations

DataTalks.Club Data Engineering Zoomcamp (In progress)

Available immediately for remote roles worldwide. 

Let's turn your raw data into decisions that move your 

business forward.