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Data Analyst with less than a year in SQL, Python, Power BI & Tableau
Data Analyst with internship experience at Nesto Digital and a portfolio of end-to-end analytics projects spanning healthcare, e-commerce, and digital marketing. Skilled in SQL, Power BI, Tableau, and Excel, with a track record of translating raw data into quantified business impact — including identifying $3.19M in revenue leakage and a 62.19% 30-day patient readmission rate. Expanding into data engineering through hands-on ETL pipeline development: built a Python-based pipeline to extract, inspect, clean, and load a 20,000-row dataset into PostgreSQL, and currently developing a containerized, Docker-based pipeline to process a 1.3M-row dataset. Building toward cloud data platforms (GCP, AWS) as the next step. Open to relocation to Lagos or fully remote roles.
University of Port Harcourt, Nigeria
Bachelor of Science · Geophysics
August 1, 2017 – June 30, 2021
Nesto Digital
Data Analysis Intern
January 1, 2025 – July 1, 2025
India
ETL Pipeline: Notebook to Containerized
June 1, 2026 – Present
Built an end-to-end ETL pipeline in Python (Jupyter Notebook) to automatically extract, inspect, clean, and load a 20,000-row CSV dataset directly into a local PostgreSQL database via pgAdmin. Currently scaling the pipeline into a containerized environment: provisioning PostgreSQL with Docker (port mapping, persistent volumes, container lifecycle management) and re-engineering ingestion to handle a 1.3M-row dataset using chunked processing with pandas and SQLAlchemy. Authored complete setup documentation (README) covering environment configuration, container start/stop workflows, and a troubleshooting guide for common connection and configuration errors. Demonstrates progression from notebook-based scripting toward production-style, containerized data engineering workflows.
Customer Retention & Revenue Analysis
June 1, 2026 – Present
Analysed full e-commerce purchase funnel for Mr Fuzzy Teddy Bear Store across 210,000+ sessions to quantify conversion losses. Identified a 54.83% drop-off rate at the Product Detail ← Cart stage, representing $3.19M in estimated lost revenue. Built multi-stage SQL CTEs to structure the funnel (Landing → Product ← Cart → Billing → Purchase) and segment by product, device, and traffic source. Conducted cohort retention analysis revealing 88.87% churn rate and $2.29M in revenue loss from non-returning customers. Modelled that a 10% improvement in Product-to-Cart conversion would generate $581K in additional revenue and 7,154 additional orders. Recommended cart abandonment recovery strategies (email/SMS) and UX improvements targeting Google Search traffic (54.93% drop-off).
Landing Page Performance Analysis
April 1, 2026 – Present
Evaluated performance of 6 landing pages (Home, Lander 1–5) using session-level data segmented by device type and traffic source. Applied Chi-square hypothesis testing in Excel to validate statistically significant differences in bounce and conversion rates across landing pages. Wrote SQL using regex-based string cleaning and CTEs to classify bounce behaviour and group landing pages for A/B comparison. Built Power BI dashboards visualising conversion rate, bounce rate, and traffic-source KPIs for stakeholder reporting. Recommended targeted A/B testing and landing page UX improvements based on device-level performance gaps.
Healthcare Encounters & 30-Day Readmission Analysis
March 1, 2026 – Present
Processed 27,890 patient encounter records (2011–2022) across Massachusetts to identify 30-day readmission drivers. Engineered key features using SQL window functions (LAG(), epoch time calculations) to compute time-between-admissions and create binary readmission flags. Identified a 62.19% overall readmission rate and pinpointed the highest-risk window as 0–7 days post-discharge, signalling early care coordination gaps. Uncovered Medicare and uninsured patients as the highest utilisation burden; chronic conditions (congestive heart failure, malignancies) as primary readmission drivers. Built interactive Power BI dashboards segmented by payer, encounter type, condition, and patient demographics.
Data Visualisation with Power BI
Utiva
January 1, 2026 – Present
Python for Data Science and AI
Utiva
January 1, 2026 – Present
Introduction to SQL for Data Science
Utiva
January 1, 2026 – Present
Cultural Fit Analysis
The candidate's portfolio projects cover diverse domains (e-commerce, healthcare, digital marketing), showcasing adaptability and a broad interest in applying data analysis. The aspiration to move into cloud data platforms and machine learning fundamentals aligns with a growth-oriented culture. The internship experience, though brief, indicates an ability to collaborate in a professional setting. The target role of Data Analyst aligns well with the demonstrated skills and project experience.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning new technologies (Docker, cloud platforms) and a clear ability to document technical processes (READMEs). Their project descriptions highlight problem-solving skills and an understanding of business impact. The psychometric test score is below average, which might indicate areas for development in logical reasoning, work attitude, stress handling, or team collaboration, but more context is needed for a definitive assessment.