Data analyst

Mihail Deligrozev

Power BI, Python and SQL. Engineer by training, analyst by trade.

See my projects Get in touch
Portrait of Mihail Deligrozev

About Me

I started as a structural engineer in Sofia, then moved to the Netherlands and did an MSc in Engineering and Policy Analysis at TU Delft, specialising in economics and finance. That mix shapes how I work: rigorous with the numbers, and focused on the decision the analysis is meant to support.

  • Education
  • Engineering
  • Data
  1. MSc Structural Engineering, UACEG Sofia 2012–2017
  2. Structural and Project Engineer, Sofia 2017–2020
  3. MSc Engineering & Policy Analysis, TU Delft 2020–2022
  4. Freelance data analyst 2023–now
The diamond marks my Microsoft PL-300 Power BI certification, June 2025.

Skills

fact_mihailMihail Deligrozev
  • power_bi_id
  • analysis_id
  • integration_id
  • visualization_id
  • tools_id
  • soft_skills_id
  • role: Data analyst
  • certified: PL-300 (2025)
dim_power_biPower BI & data modeling
  • Dashboard design
  • Slicers, drilldowns, bookmarks
  • DAX measures & KPIs
  • Power Query / ETL
  • Azure Data Factory
  • Star schema / data modeling
dim_analysisData analysis & modeling
  • Python (pandas, NumPy)
  • SQL
  • Trend analysis
  • Statistical modeling
  • Cohort analysis
  • Predictive / LTV analysis
dim_integrationData integration
  • SQL
  • Excel
  • APIs
  • Google Analytics
  • Firebase Analytics
  • App Store Connect
  • Google Play Console
dim_visualizationVisualization & reporting
  • Dash
  • Plotly
  • Matplotlib
  • Seaborn
  • Automated reporting
  • Performance dashboards
dim_toolsTools & collaboration
  • Git, GitHub
  • Jira, Trello
  • Slack
  • Excel (pivot tables, advanced formulas)
dim_soft_skillsSoft skills
  • Analytical storytelling
  • Structured decision-making
  • System-level thinking
  • Project management & coordination
  • Quality control
  • English (bilingual), Bulgarian (native), Dutch (B2)

Projects

  • What makes a Steam game successful?

    Defining success fairly across 125,855 Steam games, testing which launch-time features go with it, and predicting hits with SQL and Python.

    • Python
    • SQL / DuckDB
    • Gradient boosting
    • SHAP

  • Employee attrition: who leaves, and what would keep them

    From raw HR data to a retention strategy: segmentation, statistical tests, a predictive model and a business case with explicit assumptions.

    • Python
    • Logistic regression
    • Statistics
    • Business case

  • Do richer neighbourhoods live in greener homes?

    Household income, energy labels and neighbourhood types across the 114 neighbourhoods of The Hague, with spatial statistics and K-means clustering.

    • Python
    • geopandas
    • Spatial analysis
    • K-means

  • Do richer countries keep more children in school?

    Wealth, public spending and out-of-school rates across 217 economies, 2000–2025, using World Bank development indicators.

    • Python
    • pandas
    • SciPy
    • World Bank WDI

Get in touch

Email is the quickest way to reach me.