Bench+
← Back to talent
K

BENCH+ PROFILE

Kirill

Backend Engineer
Go backend engineering for distributed, event-driven systems

Senior backend engineer with strong Go, distributed systems, event streaming, API design, and production reliability experience across industrial automation, e-commerce, and payments domains.

BEST-FIT USE CASE

Backend roles building high-throughput event-driven services, gRPC/REST APIs, Kafka-based processing, multi-tenant platform components, and production observability in cloud or Kubernetes environments.

Not the primary fit: Roles centered primarily on frontend delivery, mobile development, product management, QA-only ownership, or model training would not match the strongest demonstrated background.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Owned backend orchestration and event-processing components for an industrial automation platform, including release and rollback responsibility.

02

Built real-time messaging with WebSocket and bidirectional gRPC streaming for distributed backend nodes.

03

Delivered Go and Node.js/TypeScript services sharing versioned Protobuf contracts and a unified delivery pipeline.

04

Implemented event-driven processing on Kubernetes and AWS Lambda with infrastructure provisioned through Terraform and cloud SDKs.

05

Worked on isolated multi-tenant deployments with mTLS, restricted network egress, and data-residency constraints.

06

Owned shared Kafka-based processing for e-commerce order, stock, availability, and catalog flows.

07

Built payment gateway backend services with gRPC, REST APIs, OpenAPI contracts, authentication, authorization, and idempotent webhooks.

08

Diagnosed production memory and streaming issues using Go profiling and improved runtime behavior.

WORKING STYLE

How they show up

  • Explains technical choices through system constraints such as latency, deployment model, infrastructure limits, and long-term maintainability.
  • Uses code review as part of team collaboration and describes reviewing architecture flow before inspecting implementation diffs.
  • Applies AI-assisted coding selectively for planning and boilerplate while keeping core logic under personal review.
  • Can work independently but expresses a preference for team collaboration and review-based transparency.
  • Approaches incidents by checking logs, runtime environment behavior, and deployment diagnostics before escalating.