Юрій Ветеран

Yurii Myravinets
Data Analyst

Junior Data Analyst with practical experience in Python (Pandas, NumPy), Excel, Google Sheets, and data
analysis in Jupyter Notebook. Skilled in cleaning and structuring data. Detail-oriented, with strong analytical
thinking and the ability to transform numbers into clear insights. Motivated to transfer extensive management
experience into the field of Data Analytics, aiming to build data-driven systems, grow as an analyst and
beyond, and support businesses in making informed decisions based on available data.

Project experience
Analytical Report on Deliveries
Tools: SQL, Google Sheets
Description: Built a delivery analytics dashboard with breakdowns by transport, regions, and months.
Analyzed 600 deliveries, identified patterns, delays, and seasonal fluctuations
My tasks:
• Collected and cleaned delivery data (successful, delayed, canceled, failed).
• Performed preprocessing: removed missing values, duplicates, and converted data types.
[открыть контакты](см. выше в блоке «контактная информация») • Grouped data by transport categories, regions, and months.
[открыть контакты](см. выше в блоке «контактная информация») • Calculated key metrics: average cost, number of deliveries, distance.
Telegram • Built charts and pivot tables to visualize results.
LinkedIn • Analyzed patterns and seasonal fluctuations, identified problem areas
Result: The dashboard reduced report preparation time and provided a foundation for managerial decision-
Zviagel, Ukraine making.

Stack Overflow Developer Survey Data Analysis
Hard skills Tools: Jupyter Notebook, Python (Pandas)
Description: Conducted analysis of a large dataset from the developer survey to identify trends in
programming language usage, compensation, and industries.
• Data analysis (data My tasks:
collection) • Data cleaning and preparation (missing values, duplicates, data types).
• A/B testing • Calculation of statistical measures (mean, median, mode, percentiles).
• ARPU, LTV, retention, • Filtering and grouping data by various criteria.
churn • Identification of the most common industries among highly paid remote workers.
• Data storytelling • Presentation of analysis results in a clear tabular format.
• Data cleaning and Result: Obtained structured insights into compensation distribution and industry trends. Demonstrated
preprocessing practical application of Pandas for business oriented data analysis, confirming skills in working with real
• Statistical analysis world datasets.
• Data visualization
SaaS Performance Dashboard
Tools Tools: Tableau Public, SQL (BigQuery), DBeaver
Description: Conducted financial analysis of a subscription-based SaaS business to evaluate revenue
dynamics and user behavior.
• Python (Pandas, NumPy, My tasks:
Matplotlib, Seaborn) • Data cleaning and aggregation in SQL via DBeaver.
• SQL • Calculation of key metrics: MRR, ARPPU, LT, LTI, Churn Rate.
• Jupyter Notebook • Building an interactive dashboard in Tableau Public with KPI cards, charts, and filters by age,
• Visual Studio Code language, and month.
• Google Colab • Visualization of revenue dynamics, user churn, expansion, and contraction trends.
• BigQuery • Applying a clear color scheme (green for growth, red for churn) for intuitive interpretation.
• Google Sheets Result: Developed a data-driven analytical tool for assessing SaaS financial performance and supporting
• Excel business decision-making.
• Access
• Looker Studio
Work experience
• Tableau
Armed Forces of Ukraine (Ground Forces)
• Power BI
Commander of a Separate Battalion
May 2022 – May 2025
Soft skills • Led a team of 500 personnel, ensuring effective coordination and successful completion of
assigned tasks.
• Analytical thinking • Established a battalion from scratch: allocated roles, organized training and supervision, which
• Self-reflection enabled rapid operational readiness.
• Teamwork • Transformed an underperforming team of 300 personnel into a cohesive unit consistently achieving
• Systematic approach mission objectives.
• Role definition • Analyzed problems and restructured the management system, increasing motivation and overall
• Continuous self-development productivity of the personnel.
• AI utilization for data analysis Achievements:
• Productivity and efficiency • Developed a management system that has remained effective for over 3 years, even after leadership
• Adaptability to new transition.
technologies • Built a team recognized as one of the most efficient within its environment

Languages Education
IT School "GoIT" Neoversity Certificate – Participant of IT CAREER CONF 25/26
English – Elementary 2025 – 2026 "Current Knowledge and IT Market Requirements 2026"
Ukrainian - Native Data Analyst