Created test automation framework form scratch. Integration tests. Rest API. Used stack: kotlin.Developed stubs for test environment: java + spring boot.Tests were configured to run with gitlab ci and docker-compose in local machines.Managed to cover critical corner cases. Created cook book for writing tests, and clear documentation that allows other engineers for fast and friendly start of using the framework.

Building and improving QA processesCreating and supporting E2E testsUsed stack: C#, Selenium, JS, Playwright. Load Testing - jmeterDeveloped integration of Slack and Telegram bots.Reduced cost and amount of bugs in prod environment, created bug notification system of prod env and tests statistics

Establishing testing processes and ensuring the quality of a platform product from scratch.Manual and automated testing, as well as web application security testing (corsa, cookie, session handling).Close interaction with the open-source project Open Service Broker API, raised issues in open source community.Developed auto tests, joining testing phase with the delivery pipeline.UI and Rest Api automation. Used stack: Java 8, Spring, Groovy, Spock, Kotlin, Selenium, Selenide, TestNg, Cucumber, RestAssured, PostgreSQL, ELK, Grafana, Prometheus. Environment to run tests: Docker, Kubernetes.Implementation of SRE practices with the development team of SLO, SLA and SLIs allowed to reduce the cost of found bugs, fixing issues, and search, which allowed to accelerate time to market delivery with fewer risks of bugs to be found by customers

Engineer in performance testing. Tuning jvm. App profiling. Writing scripts (python, LR scripts). Developing dummies (java). CI - jenkins. Performance issues analysis. Writing reports.Stack: HP Performance Center, HP Load Runner, Oracle, *nix, JAVA, Spring, JMS, XML, Python, Jenkins.Take part in the preparation of means of load testing;Conduct performance issues research of application software;Analysis of performance defects and work out the "bottlenecks" of the system.

- Markup of named entities in the ontological data system.- Using machine learning models to work with entities.- Use of technologies for text mining and data analysis.- Use of modules nltk, textblob for realization of classification of genres.- Cover the code with tests.- We used python and packages: pandas, numpy, scikit- learn, tensorflow.- Result of 95% accuracy of the classification algorithm

Conducting training on approved programs of courses in programming and developing applications in the Java language· Developed a comprehensive course, maintenance of its relevance· Developed thematic plans and methodological materials· Conducted classes in accordance to the approved timeschedule· Improved professional skills, teaching competences and communication skills