Ikechukwu Miracle is a data analyst with a passion for transforming raw data into meaningful insights that support informed decision-making. Proficient in Microsoft Excel, SQL and Power BI, and using Zapier and AI for data cleaning, analysis and insight creation.
I enjoy uncovering trends, solving problems and building interactive dashboards that help stakeholders understand performance metrics and make data-driven decisions. I started in full-stack web development, building APIs and payment integrations, and moved into data because I like finding the answers inside the numbers. I'm now focused on dissertation data analysis, reporting for small businesses, and automation that takes manual data entry off people's plates.
I'm open to AI automation and data analyst roles: remote, hybrid or onsite, and I'm willing to relocate.
Python
Zapier
n8n
Airtable
Google Forms
Google Sheets
Gmail
GitHub
Notion
Claude
CopilotGuests fill in a Google Form and Zapier sorts each response into Confirmed Booking, General Question, or Urgent Problem/Complaints. The Confirmed Booking path sends the guest an email through Gmail, and I tested it. The other two paths have their conditions in place.
An n8n workflow receives each request through a webhook, fetches its Airtable record and checks it with JavaScript. An If step then routes it: one branch updates the record and emails the customer through Gmail, the other only updates the record. Separate workflows send a weekly Airtable requests report and error alerts.
Started by cleaning the transaction data, then split it into four related tables with Python, loaded them into SQL Server and built six SQL views. Overall fraud rate is 0.386%, it peaks between 10 and 11pm, and grocery_pos is the highest-risk category. Results are in Excel and Power BI dashboards with DAX measures.
Cleaned the UCI Heart Failure Clinical Records dataset with pandas, grouped patients into age groups and built four SQL views in SQL Server. The Power BI dashboard shows 96 deaths, a 67.89% survival rate and an average age of survival of 58.76, with charts by age group for serum creatinine, ejection fraction and risk factors like smoking, high blood pressure, anaemia and diabetes.
A Power BI dashboard on hospital quality across 5.419K hospitals with an average rating of 3.21. It groups hospitals into performance tiers, compares readmission scores with complication scores by state, shows the spread of 30-day readmission rates and maps hospitals by state.
A five-table banking schema with 31 SQL tasks and SQL views. Power Query connects live to SQL Server and feeds an eight-chart Excel dashboard covering accounts per branch, loans by type, deposits by channel, loan status, balance tiers and monthly transaction trends.
Need data analysis, a dashboard built, or a process or AI automation? Send me a DM and tell me what you need.
ikechukwuamarachi003@gmail.com LinkedIn GitHub Read my documents here