After a decade dispensing medications and several years navigating real estate markets, I realized the thread connecting everything I loved about both careers was data. Not just numbers — but the stories they tell and the decisions they unlock.
I specialize in transforming raw, messy datasets into clean, reliable foundations — then using SQL, Python, and Power BI/Tableau to surface insights that actually move the needle.
Based in Bulgaria. Open to remote. Focused on building things that help people decide better.
From database design to business-ready dashboards.
Complex queries, joins, window functions, and stored procedures across SQL Server, MySQL, and PostgreSQL.
Data cleaning, transformation, and automation — turning raw files into structured, analysis-ready datasets.
Interactive dashboards and reports that turn data into clear, actionable business decisions.
Advanced pivot tables, formulas, and data analysis for rapid exploratory work.
Designing relational schemas and visualizing workflows for clean, maintainable architectures.
Modern development environment for version control, database management, and code editing.
From raw ingestion to interactive dashboards — built end-to-end.
Full ETL pipeline extracting from CSV files, parsing complex multi-value fields, and loading into a structured SQL Server database — covering drug classes, generics, manufacturers, dosage forms, and a junction table linking generics to their primary medical indications.
Quality validation built on the ETL output. Inspects every table for NULLs, duplicates, encoding artifacts, and referential integrity issues — applying targeted fixes and documenting all findings in a separate clean database.
Exploratory analysis migrated to PostgreSQL. Explores distribution, rankings, pricing, and segmentation of 21,708 medicines across 240 manufacturers and 113 dosage forms through modular analytical SQL scripts.
A two-dashboard Tableau Story covering market structure and pricing competition — KPI strips, treemaps, packed bubbles, and box plots built on 21,708 medicines across 240 manufacturers.
Python web scraper for collecting real estate listings — prices, locations, property types and features — into a structured dataset ready for cleaning and analysis.
Python-based data cleaning and transformation of scraped listings — handling missing values, inconsistent formats, outliers, and preparing a reliable dataset for analysis.
Exploratory data analysis in Python — uncovering price distributions, location trends, property type patterns and market dynamics through statistical analysis and visualisations.
Interactive Power BI dashboard presenting key insights — price trends, location breakdowns, property type distribution and market competition across the dataset.