Do you write the code or design the system?

I design the system and review the code. When a team wants hands-on implementation, I bring in senior Go engineers I have worked with for years and stay on as technical lead.

Is Go the right choice for us?

For services where throughput, predictable latency and a small operational footprint matter, usually yes. For a CRUD app with ten users, the language is not the problem. I will tell you which case you are in.

How do you handle compliance requirements in Go services?

The same way as in any stack: audit logging, encryption at rest and in transit, role-based access and least privilege, designed into the service boundaries rather than added at the edge.

What does the open-source work have to do with this?

LabraGo is the Go and GraphQL platform I created and lead. It is the reference for how I structure services, and it is public, so you can read the code before you hire me.

Go Backend Systems

Systems that hold under real traffic

Go earns its place in a backend when the system has to do a lot of work per second, with predictable latency, on hardware that does not cost a fortune. Getting there is an architecture problem more than a language problem: where the service boundaries sit, what happens when a dependency is slow, how work moves between services, and how an operator sees what is going on at three in the morning.

I design those systems and review the ones that already exist. The output is a set of boundaries, patterns and runbooks your team can keep applying after I leave.

What I design

Service boundaries. Decomposition with clear ownership and explicit contracts, so a change in one service stays in that service.
Event-driven flows. Asynchronous work over NATS or Kafka where it decouples teams and absorbs spikes, synchronous calls where they keep things simple.
Resilience. Circuit breakers, retries with backoff, timeouts, graceful degradation and health checks that mean something.
Performance. Profiling and benchmarking of the paths that matter, then the specific fixes that move p95, not a blanket rewrite.
Operability. Structured logs, metrics and traces that answer real questions, and Kubernetes-native scaling with thresholds someone has actually calculated.
Compliance-ready foundations. Audit logging, encrypted data handling and role-based access built into the service layer for healthcare and payments.

Ten times the transaction volume

A payment processor needed to grow from 5,000 to more than 50,000 transactions per hour while meeting PCI-DSS Level 1 requirements. I designed the Go microservices architecture for the payment path, isolated the cardholder data environment, and set the latency budget for each hop. The system reached the required throughput with sub-100 ms latency on the critical path, and the QSA audit passed on the first attempt.

5K to 50K+ transactions per hour, first-pass PCI audit

What you receive

Architecture decision records. The rationale for each major choice, so the team understands the why and can revisit it later.
Service boundary diagrams. Ownership maps showing how services communicate, what each one owns and where the seams are.
Performance benchmark report. Baseline measurements, bottleneck analysis and targeted recommendations with expected results.
Deployment runbook. Deployment, rollback and incident procedures written for your infrastructure.
Scaling strategy. Horizontal scaling plan with resource estimates, autoscaling thresholds and capacity projections.

Stack

Go, gqlgen, entgo, GORM, gRPC, PostgreSQL, MySQL, Redis, ElasticSearch, NATS, RabbitMQ, AWS, GCP, Terraform, Docker, Kubernetes. The reference implementation for how I structure Go services is open source: LabraGo, linked from the sidebar.