Olga Krepostnaya - Резюме Data Anaylist

Wealth-IQ Data Analyst | July 2025 – Present Key results and achievements: ETL & Data Preparation – Extracted data from SQL databases and S2 data warehouses. Performed preprocessing, cleaning, and aggregation from multiple sources to support further analysis, hypothesis generation, and decision-making, presenting results in a management-friendly format. For example, I prepared a weekly report on key revenue and client metrics, accelerating its delivery by 10x (from 4 hours to 20–30 minutes) through process automation and SQL query optimization. Visualization & Reporting – Built interactive dashboards and automated reporting templates in Excel using Power Query, pivot tables, and slicers for operational metric monitoring. Created dashboards showing both overall portfolio dynamics and client-level breakdowns. Additionally, forecasted asset changes for upcoming reporting periods using machine learning methods. Automated Partner Report Pipeline – Developed an automated pipeline for processing partner reports using NLP techniques. Data arrives in encrypted form (for security purposes); the algorithm automatically decrypts, normalizes client names (e.g., "I***v Ivan" → "Ivanov Ivan"), and prepares data for analysis. Reduced processing time from a full day to 10 minutes. Cross-team Collaboration – Actively collaborate with other teams to improve data quality in data marts. Ad-hoc Analysis – Regularly perform various ad-hoc tasks. For example, compiled summary reports on bankers, analyzed their financial metrics, and used expert assessment to identify clients with the highest churn risk. Prepared presentations for management with key findings. Fullstack Ticketing System – Developed a fullstack system for routing employee requests related to client portfolio data issues – automatically detects problem type, assigns responsible administrator, and sends instant notifications. Reduced response time from several hours to a few minutes.

National University of Science and Technology MISIS (NUST MISIS)
2023–2027
Field of Study: Computer Science and Computational Engineering

Programming & Data Analysis: Python (pandas, NumPy, matplotlib, seaborn, sklearn, CatBoost), Jupyter Notebook, FastAPI, SQL, Telegram API Databases & Tools: PostgreSQL, Git, MS Office (Excel, PowerPoint, Word) Core Knowledge: Mathematical Statistics, Mathematical Analysis, Mathematical Modeling, Data Science, Big Data

Английский

Completed projects include a microservice video analytics system (Kafka, Docker, PostgreSQL, MinIO), Telegram bot (Django + PostgreSQL), NLP name normalization model, Selenium/BeautifulSoup scrapers, and Excel dashboards. Report automation cut preparation time by 10x. English С1. Project descriptions and screenshots available upon request.