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BENCH+ PROFILE

Arnur

Backend Engineer
Go backend systems, microservices, transactional workflows, and hands-on technical leadership

Lead-level Go backend engineer suited to building and evolving backend platforms, microservices, transactional integrations, and team delivery routines.

BEST-FIT USE CASE

Backend implementation and technical leadership for marketplace, fintech, retail, or platform systems requiring Go services, service decomposition, messaging, database design, cloud deployment, and code review practices.

Not the primary fit: Roles centered primarily on frontend, mobile, QA, product design, security operations, or full-time AI model engineering.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Built and launched a Go backend for a marketplace and gaming system, then combined hands-on development with leadership of a four-person engineering team.

02

Designed and maintained gift lifecycle and integration workflows, including transactional orchestration and Outbox processing for deposit and withdrawal flows.

03

Split a monolithic partner and administration application into microservices and implemented Redis queues to reduce reliance on scaling a messaging component.

04

Developed backend services for blockchain-related gaming asset management and wallet transaction flows, with smart contract work using Solidity and OpenZeppelin standards.

05

Implemented business logic for an e-commerce achievement system and contributed to internal content-management workflow improvements.

06

Built REST API microservices for retail accounting domains and implemented asynchronous and synchronous inter-service communication using RabbitMQ and gRPC.

07

Supported a broad refactoring from Cassandra to PostgreSQL, including SQL schema design and redesigned service communication.

08

Delivered WebSocket-based push notifications and a customizable bot for technical support workflows.

WORKING STYLE

How they show up

  • Uses documentation before implementation in a specification-driven routine where concerns are reviewed by the team before development.
  • Reviews code with defined criteria and treats AI-generated code as requiring the same developer responsibility as human-written code.
  • When evaluating a complex real-time recalculation feature, tested architectural options and advised against implementation after research showed excessive complexity.