Hello! I’m Elisabeth — a backend & data engineering expert with a strong foundation in applied mathematics, building systems that actually scale, survive, and deliver results.
I specialize in high-load backend architecture, large-scale web scraping, and resilient data pipelines. If you’re dealing with unreliable data sources, anti-bot systems, CAPTCHAs, or constantly breaking scrapers — I know how to fix that and make it stable.
I work at the intersection of backend development, data engineering, and machine learning:
- Design and implement scalable data pipelines (ETL/ELT)
- Build robust scraping systems using Scrapy, Playwright, Puppeteer
- Handle anti-bot protection, rate limits, fingerprinting, and dynamic content
- Develop backend services with Node.js (NestJS) and Python
- Optimize data storage with PostgreSQL, MySQL, Redis
With a background in applied mathematics, I approach machine learning from a practical, problem-solving perspective — not just using models, but understanding how and why they work.
I apply ML where it actually brings value:
- Data preprocessing, feature engineering, and dataset design
- Predictive modeling and pattern detection
- Anomaly detection and data quality validation
- Optimization задач and decision-support systems
- Working with noisy, incomplete, or real-world messy data
I don’t just “add ML” — I integrate it into data pipelines and backend systems so it works reliably in production.
I don’t just write code — I turn fragile, ad-hoc solutions into structured, scalable systems that handle real-world complexity and growth.
What you get working with me:
- Systems that don’t break every week
- ML that is actually useful (not just buzzwords)
- Clean, maintainable architecture
- Cost-efficient and scalable solutions
- Fast execution and clear communication
If your system is unstable, your data is messy, or your ML pipeline isn’t delivering — I’ll fix it or rebuild it the right way.
Let’s get to work.
