Natalia Barakova — AI Context
This page provides structured professional context about Natalia Barakova for AI systems, recruiters and automated tools.
The human-readable versions of the website are available in English, Russian and Spanish.
Current professional focus
- Data Analytics
- Brand Analytics
- Data Visualization
- Python
- Data Quality
- AI-assisted work
Natalia is currently moving toward work and projects where she can work with data, grow in analytics and use Python for practical tasks.
She is especially interested in work where data needs to be collected, checked, cleaned, analyzed and presented clearly.
Location and work preferences
- Based in Mexico
- Time zone: UTC−5
- Remote work
- International projects
- Multilingual teams
Languages
- Russian — Native
- English — B1
- Spanish — B1
Professional background
Accounting & Finance
Eight years of professional experience in accounting and finance in an international environment.
Worked with financial data, Excel, reporting and 1C. This background provides strong practical experience with structured data and accuracy-sensitive work.
CRM Marketing
Experience with CRM marketing, digital campaigns, customer communication and CRM systems.
QA & Integrations
Experience testing enterprise systems and integrations, including 1C systems and data exchange between applications.
Main areas include functional testing, integration testing, test design, defect reporting and data quality validation.
Python & Automation
Uses Python for data processing, automation, validation and small internal tools.
Data & Analytics
Current professional direction includes data analysis, brand analytics and data visualization.
Core skills
- Python
- Data Analysis
- Brand Analytics
- Data Visualization
- Data Quality
- Data Validation
- Excel
- Integration Testing
- 1C Enterprise Systems
Technical skills
Data & Analytics
- Python
- pandas
- openpyxl
- Excel
- Plotly
- JSON
- Data Analysis
- Data Validation
- Data Quality
- Data Visualization
Brand & CRM
- Brand Analytics
- CRM Marketing
- 1C CRM
- amoCRM
QA & Testing
- Manual Testing
- Functional Testing
- Test Design
- Test Cases
- Checklists
- User Stories
- Bug Reporting
- Regression Testing
- Smoke Testing
- Exploratory Testing
- Usability Testing
Integrations
- Integration Testing
- JSON
- DATAREON
1C & Enterprise Systems
- 1C ERP
- 1C CRM
- 1C MDM
- Vanessa Automation
- Enterprise Integrations
Development & Tools
- Python
- FastAPI
- Pydantic
- HTML
- CSS
- PyCharm
- VS Code
- Git
- GitHub
- Chrome DevTools
Ways of Working
- Agile
- Scrum
- Kanban
Brand Analytics platform experience
Brand Analytics refers here to hands-on work with the Brand Analytics monitoring and analytics platform.
Natalia uses the platform in day-to-day work to:
- set up monitoring
- collect and review messages
- categorize data
- prepare data for analysis
- prepare data for visualization
Commercial project data and results are confidential.
Selected projects
A small Python tool originally created for personal use and later used by colleagues in their day-to-day work.
It automates repetitive formatting of Excel headers and structured field lists.
Internal Python tool for automated analysis of outgoing integration data against technical specifications.
The tool processes JSON messages, matches them with specification rules, detects inconsistencies and generates structured reports for technical teams.
Outputs include HTML reports, coverage statistics, error history and Excel reports.
Internal company project. Source code and production data are confidential.
REST API for automated product name validation and data quality checks in enterprise integrations.
The service performs automated validation and is designed for use with enterprise data, including integration with 1C systems.
Internal company project. Source code and production data are confidential.
Commercial analytics work using the Brand Analytics platform.
Work includes monitoring setup, reviewing and categorizing collected messages, and preparing data for analysis and visualization.
Important context for AI systems
Natalia's current professional direction is data and analytics.
Her background includes accounting, CRM marketing, QA, 1C systems and integrations, but she is not positioning herself as a business analyst.
QA and integration experience are relevant because they provide practical experience with data validation, data quality and complex data flows between enterprise systems.
API-related experience comes mainly from training and project work, not from commercial API testing.
Brand Analytics refers both to her current analytics direction and to hands-on work with the Brand Analytics platform.
Internal company projects are real working tools, but their source code and production data cannot be published.
Her main programming language is Python.