Position: Expert QA Automation Engineer | Senior Perfromance Engineer | SDET Java
Category: QA Automation
English level: fluent
Years of experience: 11

- CI process stabilization setup pipelines for 7 envs within different stages DEV, QA, STG, INT, PROD;
- An integration reporting system for 5 envs, at the real-time server;
- Setup framework test automation of multilayers: radiofrequency, API, UI, physical devices, and different OS;
- Growing feature test coverage up to 95% within 3 billions of tests;
- Decreasing regression time to 40 mins;
- The powerful functional and moduled custom framework, within a very "lazy" approach, as well structured, as any env scalable;
- Cool reporting system, integrated with Atlassian products; 
- Complex API with REST and WS test coverage up to 85%;
- Test coverage expansion even a radio frequency, and physical interface interactions with the buttons, the sliders, etc;
- Test coverage at all layers: the unit, the component, and the system, even any mod with/without UI/DB;
- Generic UI test that could be run at any of web, mob, meb, and API;
**Goals**
- QA Automation Frameworks from scratch development, empowering and maintenance based on Java or Jmeter(within full-stack monitoring: InfluxDB, Grafana, Confluence) within a layered structure, parallel running, and scalability;
- Your backend apps could and should be automated, based on requirements both functionally and non-functional, and must be measured(coverage and quality gates integration) via the benchmarking approach;
- Your most likely API(REST, WS) technologies, and any DB(SQL and NoSQL both on a board);

**Expertise**
- API and DB must be fart and furious via performance testing.
- A huge microservice like solution within 50+ services has been fully automated and benchmarked, the regression costs decrease  from 4 engineers through the months to 60 mins at CI;
- Test data generating process automation leads to the 2 million of the test cases hosted under the control version system;
- UI and Core engine integration tests automation based on the data set within more than 70 000 test cases;
- Forecasting development time based on the project management system backlogs via the different prediction models;
- CI/CD process implementation and maintenance;
- IoT devises integration to the controller testing automation via custom radio relly schema;
- IoT radio protocols network sniffing process automation;
- In general 15 clients of different vendors at the desk web, mob web, and mobile native apps through the list of backends could be automated and scaled easily;

**Interests**
Performance Analysis, Machine Learning, Altifirtishal Intelligence, Big Data, Digital Marketing;
- Microservices, load API and DB;
- Orchestration at AWS;
- BigData and benchmarking;
- IoT or Fintech projects;
- AI or ML is allowed;