QA Automation / SDET Engineer

I built and maintained approximately 1,000 automated API tests covering 80 payment-service endpoints and supported weekly production releases. I developed API, UI, and integration automation using Java, Spring Boot, JUnit, Selenide, Selenium, TypeScript, and Playwright.

I validated end-to-end payment flows across web, mobile, internal banking systems, and two API layers. I designed a Playwright framework for routing pages and web-to-mobile deep-link payment flows, investigated failed and flaky tests, prepared test data, and provided release-readiness feedback.

For payment-system migration work, I owned QA planning, regression scope, and release validation in a PCI DSS-regulated environment. I created test plans and risk-based checklists, validated Kafka events and SQL-backed processing, and mentored teammates in test design and automation practices.

I build automation-heavy products and internal tools using TypeScript, NestJS, Next.js, Python, PostgreSQL, Docker, and observability tooling.

I delivered a Qase-to-Grafana analytics integration through a scheduled NestJS service that collected Qase test results, stored metrics in InfluxDB, and visualized coverage, backlog, run duration, and automation ROI in Grafana.

I also developed a Python crawler for collecting images and metadata for an ML mockup-generation pipeline, as well as Telegram bot workflows for notifications, multi-user conversations, anonymous voting, and polling.

I developed Telemax, a full-stack influencer marketing platform connecting advertisers, influencers, and administrators.

I built backend services with NestJS and Fastify, using PostgreSQL, Prisma, Redis, MinIO, OpenTelemetry, token-based authentication, and OpenAPI-generated TypeScript SDKs.

I implemented campaign and channel workflows, file storage, data-provider integrations, and CI-ready testing, linting, and build pipelines within a pnpm monorepo.

I built and deployed an image style transfer web application for a small group of students.

I developed the application with Python, Flask, and PyTorch, implementing neural style transfer functionality.