Chiedozie Nnadozie

Optimizes Power BI dashboard reliability and manages data refreshes to ensure high availability during peak decision-making. Improves data integrity across SQL/Snowflake/ERP, automates reporting pipelines, and builds anomaly detection for faster incident detection.

Delivered end-to-end data products translating complex business questions into technical solutions. Worked across Excel, Power BI, Tableau, SQL, and Python, including ML and cloud-related exploration.

Optimized SQL query architecture by replacing inefficient subqueries with joins and temporary tables to improve execution speed. Strengthened SQL skills through hands-on project-based work in a remote setting.

Performed data cleaning, preprocessing, and feature engineering using Python on structured datasets. Built visualizations with Seaborn to extract actionable insights.

Collaborated on a Speech Emotion Recognition model using Jupyter Notebook and deep learning. Engineered features from .wav audio data and deployed a local Flask app for predictions.

Delivered tailored data solutions for virtual clients using SQL, Excel, Python, and Power BI, supporting reporting and dashboard needs. Produced interactive reports to aid client decision-making.

Designed a SQL-based system to log and monitor EFTs from POS, ATMs, and online platforms. Implemented relational storage with stored procedures/queries for fraud detection and performance tracking, and generated realistic transaction data using Python Faker.

Cleaned and prepared datasets using SQL techniques including regex and window functions to address nulls and duplicates. Performed SQL-based EDA on layoffs data to uncover trends and reasons.

Analyzed telecom customer data from Kaggle to identify churn customers. Transformed data with DAX, visualized results in Power BI, and delivered recommendations to improve retention.

Bachelor of Engineering in Mechanical Engineering at Federal University of Technology, Owerri, completed in December 2023.