About Me

Hi, I’m Jethro – a Data & Business Intelligence Analyst.
I turn raw data into strategic insights and automate reports that seamlessly connect to all relevant data sources.

With a background in HR analytics and business strategy, I specialise in:Data extraction & cleaning – Refining raw data into structured datasets for analysis.
Data analysis & forecasting – Analysing data to uncover patterns and forecast trends
Data visualization & storytelling – streamlining reporting systems to save time and enhance accuracy.

I thrive at the intersection of data, decision-making, and business transformation, ensuring businesses make more informed decisions.

Skills

Excel | Python | Tableau | PowerBI | SQL

  • Data Gathering & Cleaning

  • Data Analysis & Interpretation

  • Data Visualization & Storytelling

  • SQL & Database Management

  • Business Intelligence & Reporting Automation

  • KPI & Performance Tracking

Featured Projects

MS EXCEL | DATA TRANSFORMATION & REPORTING

Excel empowers my data analysis and BI workflows, enabling data cleaning, analysis, and interactive reporting with dynamic dashboards.

SQL & PYTHON | The Complete Toolkit

Built a comprehensive collection of SQL exercises covering joins, table creation, data insertion, triggers, window functions etc.

Power BI | NHS Hospital Admissions Dashboard

Developed an interactive dashboard to analyze 12.8M admissions, emergency cases, and patient demographics to support hospital resource planning.

Tableau | Superstore Sales Dashboard

Developed a dynamic sales dashboard tracking revenue, regional trends, and category performance.Visualized sales by state, segment, and sub-category to drive data-driven strategies.

SQL | Global Layoffs Analysis (2020-2025)

Processed and analyzed large-scale layoff data using SQL, refining datasets to reveal workforce trends from 2020-2025. Provided structured insights into the industries and companies most affected over time.

TABLEAU | HR ANALYTICS DASHBOARD

Streamlined HR reporting with a dynamic Tableau dashboard that transforms raw data into actionable insights.

Certifications

  • Google Data Analytics Certificate

  • Google Business Intelligence Certificate

  • HR Data Analyst; AIHR

  • People Analytics Specialist; AIHR

  • PowerBI Data Analyst

MS EXCEL | DATA TRANSFORMATION & REPORTING

This chart showcases my ability to analyze and visualize workforce data using Excel, turning complex numbers into clear, actionable insights. Using Power Query and Pivot Charts, I structured the data to highlight representation trends across corporate roles. My goal is to make data easy to understand, helping businesses make informed, data-driven decisions.

This visualization reflects my ability to analyze financial data using Excel, breaking down departmental budget utilization in a clear and digestible format. By leveraging Pivot Tables, Power Query, and advanced charting, I transformed raw budget data into an easy-to-read comparison of spending versus allocation. This type of analysis helps organizations track financial efficiency, identify cost-saving opportunities, and make informed budget decisions.

This visualization demonstrates my ability to analyze industry trends using Excel, tracking mergers and acquisitions (M&A) activity in the tech sector before and after COVID-19. By leveraging Pivot Charts, data aggregation, and trend analysis, I identified shifts in corporate strategies over time. This kind of insight helps businesses and investors understand market dynamics, investment patterns, and strategic decision-making in a rapidly evolving industry.

TABLEAU | HR ANALYTICS DASHBOARD

OverviewThis interactive Human Resources Dashboard provides a comprehensive real-time view of key employee metrics, enabling HR managers to track trends, identify insights, and make data-driven decisions.With a dynamic, filterable interface, users can explore various aspects of employee demographics, recruitment, salary distributions, and department performance all in one centralized view.

This dashboard captures key HR KPIs such as:Employee Count & Turnover – Tracks total employees, hires, and terminations to monitor workforce trends.
Employees by Gender & Age – Offers insights into gender representation across different age groups.
Departmental Breakdown – Displays the number of employees per department, with a visual representation of new hires vs. terminations.
Top Hiring Locations – Highlights geographic hiring hotspots, crucial for expanding recruitment strategies.
Recruitment by Education & Age – Analyzes the educational background of recruits across different age groups.
Salary Insights – Compares salaries by education level, gender, and role, helping identify pay disparities or trends.

Dynamic & Interactive FeaturesThe dashboard is fully filterable and dynamic, allowing users to drill down into specific metrics based on department, age group, gender, education, and geography. As showcased in the loop video above, users can interactively adjust parameters to explore workforce trends in greater depth, making it an adaptable tool for HR teams, executives, and recruiters.Key Takeaways1. Employee Growth & Retention: With 8,950 hires vs. 966 terminations, workforce growth remains strong.
2. Gender Representation: The workforce is 54% male and 46% female, with balanced representation across age groups.
3. Salary Trends: Gender pay gaps are visible at bachelor and master’s levels, highlighting areas for compensation review.
4. Hiring Hotspots: Locations such as New York and Pennsylvania show high recruitment activity, guiding regional hiring strategies.

SQL | Global Layoffs Analysis (2020-2025)

This project aims to clean, transform, and analyze global layoff data from March 2020 to July 2024 using SQL, ensuring data accuracy and extracting meaningful insights.
It showcases my proficiency in SQL-based data cleaning, ETL processes, exploratory data analysis (EDA), and business intelligence applications. By structuring and analyzing workforce trends, this project demonstrates my ability to turn raw data into actionable insights for strategic decision-making.

Data Cleaning & PreprocessingThe first step in this project involved cleaning and preparing the dataset for analysis using SQL. It focused on:1. Standardizing Data Formats – Ensuring consistency in date formats, company names, and categorical values.
2. Handling Missing Values – Applying methods such as imputation and removing records with insufficient data.
3. Deduplication – Identifying and eliminating duplicate records to maintain data accuracy.
4. Data Integrity Checks – Filtering out inconsistencies and ensuring fields align with expected data types.
5. Schema Optimization – Renaming and restructuring columns for better readability and analysis.
This process resulted in a clean, structured dataset, enabling reliable and insightful analysis of global layoff trends.

With a refined dataset, exploratory data analysis was conducted focusing on:1. Industry Trends – Identifying which sectors experienced the highest layoffs.
2. Geographic Distribution – Analyzing layoff frequency across different regions.
3. Company-Specific Impact – Aggregating layoffs at the company level to detect major contributors.
4. Time-Series Analysis – Examining how layoffs evolved from March 2020 to July 2024.
5. Key Summaries – Computing key metrics such as total layoffs, average layoffs per company, and annual trends.

SQL & PYTHON | The Complete Toolkit

OverviewThis project is a comprehensive SQL toolkit designed to showcase advanced database management and data wrangling techniques for relational databases. It contains diverse SQL queries and scripts that cover table creation, data manipulation, joins, triggers, window functions, and deduplication strategies, demonstrating my ability to transform raw data into structured, insightful, and actionable datasets.

  • Database Design & Data Modeling – Creating and managing relational database schemas, defining table structures, and ensuring data integrity for optimized querying.

  • SQL for Data Extraction & Transformation (ETL) – Writing efficient SQL queries for data wrangling, including joins, CTEs, subqueries, and case statements to structure raw data for analysis.

  • Advanced Querying & Window Functions – Leveraging RANK, ROW_NUMBER, LAG, LEAD, and NTILE to perform analytical calculations and time-series evaluations.

  • Data Cleaning & Deduplication – Using SQL and Python (Pandas, NumPy) to remove duplicates, handle missing values, normalize data formats, and ensure high-quality datasets.

  • Automation & Workflow Optimization – Implementing SQL triggers, stored procedures, and Python scripting for data pipeline automation and scheduled reporting.

  • Business Intelligence & Visualization – Preparing structured data for Power BI, Tableau, and Matplotlib/Seaborn, enabling data-driven decision-making through actionable insights.

I have a collection of SQL and Python projects on my GitHub, showcasing my expertise in data cleaning, transformation, analysis, and visualisation.These projects range from SQL-based data wrangling/cleaning, ETL workflows, and business intelligence reporting to Python-driven data analysis.Each repository highlights my ability to handle real-world datasets, extract meaningful insights, and build scalable data solutions.You can explore my work and see how I apply data-driven problem-solving techniques by visiting my GitHub profile here. 🚀

Power BI | NHS Hospital Admissions Dashboard

OverviewThis interactive NHS Hospital Admitted Patient Care Activity Dashboard provides a data-driven view of hospital admissions, patient demographics, and healthcare service utilization across various specialties.Designed to help healthcare administrators, policymakers, and researchers, this dashboard enables a deep dive into patient episodes, waiting times, length of stay, and types of admissions for the year 2020-2021.

Key Metrics TrackedTotal Admissions: 12.8 million, indicating overall hospital inflow.
Emergency Cases: 5.5 million, reflecting critical care demand.
Total Finished Consultant Episodes (FCEs): 16.2 million, showing hospital case volume.
Mean Length of Stay (LOS): 5.8 days, providing insights into hospital bed utilization.
Mean Patient Age: 49.3 years, offering a demographic perspective on hospital admissions.
Mean Time Waited: 72.3 minutes, showing patient waiting times before receiving care.

This interactive dashboard allows users to filter and explore:Admissions by Specialty – Understand patient inflow across different medical specialties.
Gender-based FCE Analysis – Compare the volume of cases across male, female, and unknown gender categories.
Length of Stay & Waiting Times – Assess hospital efficiency and patient throughput.
Types of Admissions – Breakdown of cases into waiting list admissions, emergency cases, and planned visits.
Episodes vs. Mean Age – Visualize the relationship between patient age and consultant-led treatment episodes.

Key Takeaways:🔹 High emergency case volume (5.5M cases) indicates the need for optimized emergency response strategies.
🔹 Specialties such as General Medicine and Surgery have the highest finished consultant episodes, requiring more specialist workforce planning.
🔹 Waiting list admissions (3.8M) suggest potential delays in scheduled patient care services.
🔹 Patient demographics show a mean age of 49.3 years, implying a strong need for elderly-focused care strategies.

Tableau | Superstore Sales Dashboard

OverviewThis Super Store Sales Performance Dashboard provides an interactive and dynamic view of key sales metrics, offering actionable insights into regional performance, product category trends, top sales contributors, and customer segments. Designed for business leaders, sales strategists, and data-driven decision-makers, this visualization enables the identification of trends, bottlenecks, and revenue-driving opportunities.Leveraging SQL-based data extraction and cleaning, advanced analytics, and visual storytelling, this dashboard transforms raw sales data into a strategic asset.

This end-to-end data analytics solution showcases my expertise in business intelligence, SQL-driven data engineering, and advanced visualization techniques. Whether it’s data extraction, transformation, or storytelling through insightful dashboards, I bring a strategic approach to turning complex datasets into actionable business intelligence.