Марія

Mariia Kutsenko
Junior Data Analyst
[відкрити контакти](див. вище в блоці «контактна інформація») [відкрити контакти](див. вище в блоці «контактна інформація») [відкрити контакти](див. вище в блоці «контактна інформація») Bielsko-Biala, Poland

SUMMARY

Junior Data Analyst with a strong analytical mindset and hands-on experience in data analysis, reporting, and process
optimization. Skilled in SQL, Python, and data visualization tools. Experienced in logistics and operations, with a proven
ability to analyze performance metrics, improve workflows, and support data-driven decision-making. Currently
expanding expertise in data analytics through professional training and personal projects.

SKILLS

HARD SKILLS
SQL, Python, A/B Tests, Data Analysis, Data Cleaning, Data Visualization, Reporting, KPI Tracking, Process
Optimization, konversion, ARPU, LTV, retention, churn, Data Storytelling, statistics.
TOOLS
Pandas, NumPy, Matplotlib, Seaborn, Google Sheets, Tableau, Power BI, BigQuery, Jupyter Notebook, Microsoft Office,
SAP, Excel, Looker Studio, Amplitude, GitHub.
SOFT SKILLS
Responsibility, Stress resistance, Analysis and decision making.

PROJECT EXPERIENCE

Global E-Commerce Sales & Profitability Performance Dashboard
https://github.com/mariasitab96-jpg/powerbi-global-sales-analysis.git
Tools: Power BI Desktop, Power Query (ETL), DAX (Data Analysis Expressions), Data Modeling (Star Schema), Data
Visualization & UX/UI Design.
Description: Developed an end-to-end interactive Business Intelligence dashboard analyzing global retail operations,
dynamic revenue trends, and regional profitability. The solution processes multi-year transactional datasets (over 25K+
total orders) and transforms them into granular, actionable insights across product categories, geographic regions, and
customer pricing segments to support data-driven executive decision-making.
My tasks:
●​ Data Extraction & Transformation (ETL): Ingested, cleaned, and profiled raw transactional data using Power
Query; handled missing values, optimized data types, and constructed automated query steps.
●​ Data Modeling: Designed an efficient relational Star Schema model, creating seamless relationships between the
central Fact table and customized Dimension tables (Calendar, Geography, Products).
●​ Advanced Analytics with DAX: Developed complex DAX measures, calculated columns, and Time Intelligence
functions to calculate key business metrics, including dynamic Profit Margins, Year-over-Year (YoY) Profit
Growth %, and running totals.
●​ Interactive Visualization: Built a user-centric, 4-page responsive report incorporating line charts for trend lines,
bar charts for Top-10 analysis, scatter plots, and multi-level performance matrixes.
●​ Business Insight Exploration: Conducted deep-dive analysis on discounting behavior to uncover specific
thresholds where pricing strategies negative-impacted corporate profits.
Result: Successfully centralized and visualized $12.64M in total sales and $1.47M in net profit spanning multiple global
regions. Provided executive transparency into core operational KPIs, highlighting a 61.95% YoY Profit Growth and an
overall 11.61% Profit Margin. Formulated a dedicated Discount Impact Analysis page that mathematically proved
discounts above 30% led to severe profitability drops (-$958K), enabling leadership to adjust and optimize global discount
policies. Automated dynamic reporting across years, quarters, and months, completely eliminating manual reporting
overhead and spreadsheet errors.

GA4 Ecommerce Funnel Data Analysis
https://public.tableau.com/views/Googleanalytics4Salesfunneldashboard/Dashboard1?:language=en-US&:sid=&:re
direct=auth&:display_count=n&:origin=viz_share_link
https://console.cloud.google.com/bigquery?sq=903325080812:5e55f09fe5d244bdaa3a8f87fe2ed02c
Tools: SQL (BigQuery), Tableau
Description: I developed an interactive dashboard to analyze the user conversion funnel for an ecommerce store using the
public GA4 dataset in BigQuery. The goal was to provide marketing managers with actionable insights into user behavior
and store performance.
My tasks:
●​ Extracted and transformed raw GA4 data using SQL (BigQuery) to define custom funnel steps and user sessions.
●​ Calculated key performance indicators (KPIs) including Total sessions, Purchases, Overall conversion rate.
●​ Built a comprehensive conversion funnel (from session start to purchase) to identify drop-off points.
●​ Created interactive visualizations including TreeMaps for OS distribution, Pie charts for device categories, Trend
lines for conversion dynamics.
●​ Implemented multi-layered filters (Date, Language, Traffic Source) to allow deep-dive analysis into specific
audience segments.
Result: I created a data-driven tool that identified a 21% drop-off between session starts and product views, highlighting
areas for landing page optimization. The dashboard enables stakeholders to independently track efficiency across various
traffic sources and device types.
Delivery Analytics
https://docs.google.com/spreadsheets/d/1YbyQBPLp5hE2k5r24CUoWNfF_F76hhlIaEbhgCJBk1k/edit?usp=sharing
Tools:
Google Sheets, SQL, Data Visualization, BigQuery
Description:
Analysis of delivery service performance using order, region, weather, and transport data.
My tasks:
●​ Prepare and analyzed delivery data.
●​ Wrote SQL queries to calculate metrics.
●​ Analyzed delivery status and regional performance .
●​ Studied seasonality and weather impact.
●​ Created charts and a dashboard.
Result:
Identified problem regions, delay factors, and ideas to improve logistics.
Stack Overflow Developer Survey 2025 Data Analysis
https://github.com/mariasitab96-jpg/Project/blob/main/Kutsenko_project2%20(2).ipynb
Tools:
Python, Pandas, NumPy, Jupyter Notebook
Description:
I analyzed the Stack Overflow annual survey to find trends in programming languages, work experience, and salaries.
My tasks:
●​ Loaded and cleaned large CSV data files.
●​ Counted unique respondents using the .nunique() method.
●​ Analysed Python popularity and remote work trends.
●​ Calculated statistics (mean, median, mode) for work trends.
●​ Grouped data by country to find the average salary for Python developers.
Result:
I created a data report in the Jupyter Notebook. I cleaned “NaN” (missing) values to make salary calculates more accurate.

WORK EXPERIENCE

Team Leader
Hutchinson Poland Sp. z o.o.
January 2020 - Present / Bielsko-Biala, Poland
●​ Supervise daily operational processes and coordinate team performance.
●​ Monitor workflow efficiency and ensure adherence to production and logistics.
●​ Track performance indicators and prepare operational reports.
●​ Work with internal systems (including SAP) to manage operational data.
●​ Support data-driven decisions by providing structured reports to management.

EDUCATION
Data Analyst
IT School GoIT
October 2025 - Present
Master of Engineering in Transport Logistics
The Upper Silesian Academy of Wojciech Korfanty
2018 - 2022 / Katowice, Poland

LANGUAGES

English - Intermediate Ukrainian - Nativе Polish - Upper-intermediate