Дмитро Ветеран 

DMYTRO KHRYSTIUK
DATA ANALYST

CONTACT SUMMARY

[открыть контакты](см. выше в блоке «контактная информация») Motivated Data Analyst proficient in SQL, Tableau, and Python. Analyzed user
activity and email metrics (Open Rate, CTR) to segment audiences by engagement.
Lutsk, Ukraine, remote, relocate Evaluated behavioural factors and conducted geographic market analysis to identify
growth opportunities and scaling priorities. Leveraging a strong background in sales
LinkedIn and finance, I tripled a client base and developed custom sales methodologies.
Experienced in mentoring junior specialists. Upper-Intermediate English. Eager to
Telegram contribute analytical expertise to innovative, collaborative projects.

[открыть контакты](см. выше в блоке «контактная информация»)
WORK EXPERIENCE

SKILLS Military Service of Ukraine, Kyiv June 2023 – March 2025

SQL: advanced (mainly BigQuery) Finance Department / Accountant

Python: Pandas, NumPy, Matplotlib, Data Management: Processed large-scale payroll and financial datasets with
Seaborn, Exploratory Data Analysis 100% accuracy.
Compliance: Interpreted complex regulations to ensure data integrity and audit-
(EDA) ready financial reporting.
BI: Tableau, Looker Analysis: Maintained financial records and performed budget analysis to
optimise workflows.
Statistics: Sampling, Hypothesis
Testing, Correlation, Statistical ViYar October 2017 – June 2023
Analyst / Senior Key Account Manager
Significance
Strategic Growth: Managed 150+ accounts ($1.4M+ ARR) and tripled the client
Excel/Google Sheets: advanced
base.
A/B Testing: Planning, Analysis, Sales Analytics: Utilised ABC analysis and trend identification to boost
Reporting engagement by 73%.
Performance: Twice awarded "Manager of the Year" for top client acquisition
Other tools: Google Analytics, Git
and portfolio expansion.
Tools: 1C, Excel, Viyar Pro (online manufacturing platform).

LANGUAGES EDUCATION

English (Upper-Intermediate) Self-education in analytics March 2025 - Present

Business case study: Comprehensive analytics for a retailer
Goal: Configure self-service BI tools and get actionable business insight.
Data source: BigQuery DWH (sales, marketing, logistics data).
Metrics: GMV, Net Profit, ROI, Delivery Lead Time, CAC, CR.
Dashboards (Tableau/Looker Studio): Interactive sales dynamics dashboards
with automatic “red flags” to monitor anomalies.
SQL Modeling: Identified growth points - optimizing logistics routes and
launching advertising campaigns on weekends/holidays.
Python Automation (Pandas/NumPy): Optimized ETL data cleansing processes,
which accelerated reporting.

Master’s in Audit and Accounting October 2010 – March 2013
Alfred Nobel University
 PORTFOLIO:

Data Analyst

Project: Comprehensive Sales & User Behavior Analysis (Python)

Objective: To identify key revenue drivers and segment sales by geography and device type to optimize marketing
spend.
Result: Developed an automated analytical report. Identified high-profit categories and regions with growth
anomalies, enabling data-driven strategic decisions.
Context: Handled fragmented data on sales, product categories, and device types within a Google Colab
environment.
Tech Stack: Python (Pandas, NumPy), Matplotlib, Seaborn.
Link: to review

Project: Interactive Sales Performance Dashboard (Tableau)

Objective: To visualize sales time-series data and build a tool for instant filtering by individual product performance.
Result: Created a dynamic dashboard that reduced weekly reporting time by 80%. The tool enables management to
detect sales drops for specific items in real-time.
Context: Managed large volumes of transactional data that were difficult to track and analyze in static spreadsheet
formats.
Link: to review

Project: Conversion Funnel Analysis & Customer Segmentation (SQL)

Objective: To perform data cleansing and segment the user base by activity levels and traffic sources.
Result: Wrote complex SQL queries (CTEs, Window Functions) to calculate conversion rates. Identified the most
loyal user segments, providing a foundation for personalized marketing campaigns. Results visualized in Looker
Studio.
Context: Required processing of raw, multi-table data from BigQuery to evaluate the effectiveness of various
advertising channels.
Link: to review