I’m Simon Niyomugabo, a data analyst and SQL developer who loves building clean datasets, insightful dashboards, and analytics workflows that actually move business metrics.
Who I am and how I think about data.
I’m Simon Niyomugabo — a Data Analyst and SQL Developer who turns complex data into clear, actionable insights. I specialize in SQL, Power BI, and Python, and I build analytics solutions that help teams make smarter, faster decisions. I take pride in solving real business problems with clean data, strong logic, and meaningful visualizations.
My favorite work sits at the intersection of SQL, analytics, and business impact—whether that’s understanding why customers churn, where revenue is leaking, or which products are quietly driving growth.
I approach problems by first clarifying the question, then exploring the data, and only then building dashboards or reports. I value reproducible workflows, clean SQL, and documentation that others can follow.
Tools and capabilities I use to deliver insights.
Realistic, SQL‑driven analytics work with clear business outcomes.
Built a complete analytics workflow to evaluate sales performance across regions, product categories, and time periods. Cleaned and transformed raw CSV data using SQL, then visualized insights in Power BI.
Impact: Identified seasonal demand patterns and uncovered a 12% revenue opportunity by optimizing inventory for high‑growth regions.
A complete SQL analytics case study exploring retail sales, customer behavior, and product performance using a multi‑table SQLite database. Includes 15 business‑focused queries, clear insights, and a fully reproducible workflow.
Analyzed customer subscription data to identify churn drivers. Used SQL to clean and segment customers, then performed churn analysis and built a simple predictive view.
Impact: Revealed that customers with low engagement in the first 30 days were 3.4× more likely to churn, helping shape a targeted retention strategy.
Developed a reusable SQL script library to clean messy datasets: remove duplicates, standardize formats, validate data types, and flag anomalies before analysis.
Impact: Reduced manual data‑cleaning time by 80%, enabling faster reporting cycles for analytics teams.
Designed a relational database schema for an online store, including tables for customers, orders, products, and transactions. Implemented indexing and normalization to improve performance.
Impact: Improved query performance by 40% and ensured reliable data for analytics and reporting.
Let’s talk about data, dashboards, and SQL.
If you’re looking for someone who enjoys working directly with SQL, cleaning data, and building clear analytics outputs, I’d be happy to connect.
I’m especially interested in roles focused on data analytics, BI, and SQL‑heavy workflows where I can help teams make better decisions with trustworthy data.
Prefer email or LinkedIn—happy to share more details, walk through these projects, or discuss how I think about data.
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