Алла

ALLA NAKONECHNA
DATA ANALYST
Tel: [відкрити контакти](див. вище в блоці «контактна інформація») [відкрити контакти](див. вище в блоці «контактна інформація») Travagliato, Lombardy, Italy
Telegram: https://t.me/i_allaanatolievna
[відкрити контакти](див. вище в блоці «контактна інформація»)

ABOUT ME
Junior data analyst. I have knowledge of SQL, Python, Google Sheets, and visualization tools such as
Looker Studio and Tableau. I have experience working with large amounts of data, building reports and
dashboards. Analytical skills, attention to detail, and the ability to find insights that contribute to
business decision-making.
HARD SKILLS
DBeaver, Python
PostgreSQL, BigQuery
Google Sheets, Exel
Looker Studio, Tableau
PROJECTS
“GoIT-Data Analysis and Marketing Effectiveness Assessment”
Tools: SQL, BigQuery, Google Analytics 4, PostgreSQL, DBeaver.
Task: Calculate relative and absolute growth metrics for the period. Extract the necessary
characteristics and data from the Google Analytics dataset. Calculate Google Analytics metrics.
My actions: Connected to a public dataset. Wrote an SQL query to process large amounts of data in
BigQuery. Performed a comparative analysis of changes in key metrics over different periods.
Interpreted the data to identify factors influencing the growth/decline of metrics and formed
conclusions for improving online companies.
Conclusions: Based on the analysis, key factors influencing the change in metrics were identified. This
allowed us to formulate specific recommendations for improving the effectiveness of marketing
campaigns and optimizing the budget.
Link to the project:
https://docs.google.com/presentation/d/1wHjrhelCvVFV48WRB1BGa3YqG_hWGikBESmhbNbIBOI/edit
?usp=sharing
"GoIT-Delivery Analysis"
Tools: Tableau
Task: Build a time series of sales and profits based on Superstore Sales data. Analyze profitability by
subcategory and delivery method. Investigate order delivery times and visualize them on a map of the
United States.
My action: Calculated metrics directly in BI systems. Built time series data. Segmented data in BI
systems. Configured user filters. Created interactive dashboards.
Conclusions: The analysis revealed sales and profit dynamics, identified profitable product
subcategories and delivery methods. Visualization of delivery times on a map of the US helped identify
regional delays.
Interactive dashboards with filters provided flexible analysis for users.The insights gained became the
basis for improving business processes and logistics.
Link to the project:
https://public.tableau.com/views/DeliveryAnalysis_17558552101130/DeliveryDaysbyState?:language=e
n-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link
WORK EXPERIENCE
“Sens-Bank” December 2017-March 2019
Cashier at branch № 279 in Zhytomyr
Recalculation and sorting of cash from collections and internal departments
Verification of the authenticity of banknotes, preparation of cash documentation
Loading and unloading ATMs, working with cash cassettes
Working with cash transactions and banking systems
Compliance with NBU requirements for cash storage and circulation
Maternity leave April 2019-May 2022
Care for the elderly July 2022-November 2025
EDUCATION
IT School “Go IT” | 2025
Data Analisys
Polissia National University |
September 2017-December 2018
Master's Degree in Finance, Banking, and
Insurance
LANGUAGES
English - elementary
Italian - intermediate
Ukrainian - native