Oleksander Pyavchik | Manual QA Engineer Manual QA Engineer portfolio

QA Artifact

SQL Reference and Database Validation for pyavchik.space

Hands-on SQL and database-validation artifact showing how PostgreSQL schema, queries, constraints, indexes, and transactional behavior were used to validate API and backend data consistency during QA work.

Artifact content

Public sample: pyavchik.space/sql.html

This artifact is a public SQL and PostgreSQL database reference used as evidence of backend data understanding during QA work. It includes a schema-oriented view, connection-pool configuration, source-file mapping, and 30 query references across DDL, DML, and transaction-oriented operations.

The page documents practical database concerns rather than abstract syntax only. It includes parameterised queries, atomic token rotation, unique constraints, composite indexes, optimistic locking, BIGINT cents, transactional round creation, and provably fair seed handling.

  • Shows how auth, wallet, token, and round data are modeled and persisted
  • Provides examples of CREATE TABLE, CREATE INDEX, SELECT, INSERT, UPDATE, DELETE, and transaction-style operations
  • Helps validate API behavior against actual storage assumptions, not only response payloads
  • Supports recruiter-visible proof of SQL literacy, schema reasoning, and database-related QA thinking

This is useful portfolio evidence because it shows that I can read and reason about the data layer, constraints, and persistence risks behind a web application, not only test the UI or API surface.

Artifact details

Type: SQL / Database

Objective: Show practical SQL and database understanding as part of QA work, including schema reasoning, constraints, persistence behavior, and backend data validation.

Coverage: Schema view, connection-pool details, source-file references, 30 query examples, indexes, transactions, constraints, and database best practices relevant to auth, wallet, rounds, and seeds.

Tools

  • SQL

Recruiter note: Demonstrates database-aware QA thinking through schema reading, query review, constraints, indexing, token rotation, and persistence-focused validation.