Position: Backend Python Developer
Category: Python
English level: intermediate
Years of experience: 3


I have experience designing and working with:

1) OOP and functional programming;
2) competitive programming using asynchrony, multithreading and multiprocessing;
3) ETL processes and their integration with third-party systems using Python;
4) web parsers that collected data from large ecommerce projects in Python;
5) auto-reporting systems in Python, Bigquery and Looker Studio;
API integration from Google;
6) deploying projects on Google Cloud Functions and Google Virtual Machines;
7) engineering large and complex data volumes with Pandas, NumPy, Dask and SQL;
8) PostgreSQL database;
9) automatic testing of sites using Selenium;
10) Google Analytics, Google Ads, Google Tag Manager, Campaign Manager, Display Video 360, Facebook Ads;
11) basic concepts of web development;
12) web applications and REST API on Django, DRF, FastAPI, Celery and Redis;
13) writing code in JS for the frontend and backend;
14) building machine learning models on scikit-learn;
15) mathematical verification of statistical hypotheses of the behavior of the Ukrainian real estate market thanks to statistical models.

MY EXPIRIENCE:

1) Python developer, database administrator

1.Created, maintained and extended an asynchronous anonymous web scraper for two popular marketplaces.
2.Administered PostgreSQL database and Linux servers.
3.Analyzed and created reports on the Ukrainian real estate market.
4.Mathematical analysis and testing of hypotheses of the behavior of the real estate market in Ukraine
5.Partially automated the technical process of real estate appraisal.
6.Built ML models to predict the market price in the real estate market based on real data from marketplaces.
7.Designing simple REST APIs to provide data to clients.
8.Writing a simple web application for real estate appraisers.

2)Analyst and Python Backend developer

1.Building an ETL pipeline in Python and Google Cloud.
2.Maintained, improved performance and extended the autoreporting project in Python, Bigquery and Looker Studio.
3.Integrating a large number of APIs, primarily from Google, Meta and payment services Wise and Paysera.
4.Created dashboards in Looker Studio.
5.Automation for the collection and analysis of data related to advertising.
6.Configured Google Tag Manager using javascript.
7.Writing App Scripts for automatization.
8.Configured Google Analytics and Google Optimize.
9.Using Google Cloud Functions and Google Virtual Machines as a backend.
10.Data engineering of large and complex volumes of data.