Event-Driven developer; music enjoyer; systems whisperer.
I build performant, scalable, and easily maintainable tools by leveraging the unique strengths of my core technology stack. Whether it's high-speed backend services or complex data processing, my ecosystem is designed to work together seamlessly.
I use the handle enqack on Github and produce music under the name Hellfonic.
From bash to zcat. From DNS to email. I possess the skills to configure and administer your systems.
Whether it is consensus or discovery I can navigate a route through the hive.
From build tooling to design patterns I can craft anything given the resources.
A unique, declarative Linux distribution built on top of the Nix package manager, where the entire operating system configuration—including packages, system services, and configuration files—is managed via a single functional language.
Websockets, QUIC are of prime interest.
Whether it is NixOS or Terraform and Ansible I have the experience to overcome your deployment gaps.
A frontier landscape I enjoy exploring.
Powering high-concurrency backend services, CLI tools, and systems that require raw performance and fast execution.
The connective tissue of the organization. Protobufs maintain strictly typed, language-agnostic, and lightning-fast communication contracts.
The province of data processing, scripting, automation, and rapid prototyping within the ecosystem.
Go and Python with Protocol Buffers is a proven stack for high-performance, cross-language microservices. Go owns performance-critical services; Python handles data science, ML, and automation. Communication is anchored by a centralized .proto file defining shared data structures and service contracts, from which language-specific message types, gRPC interfaces, and client stubs are generated for both languages. The runtime layer is completed by the standard protobuf library via PyPI for Python and Google's official protobuf modules for Go — giving a consistent, efficient serialization foundation across both languages.
The best systems are the ones nobody notices - until they're gone.
Good software engineering is not a collection of techniques - it is a disposition toward systems. It is the habit of asking what a thing actually does before asking how to make it faster, the reflex to name failure modes before celebrating success paths, and the discipline to leave a codebase more legible than you found it. These are not taught in a curriculum. They are earned through years of maintaining systems that outlive their original authors, debugging production failures at 2 AM, and learning - repeatedly - that complexity is not a sign of sophistication but of deferred decisions.
I have spent more than two decades cultivating that disposition. My career traces the full vertical of modern infrastructure: from the machine-level concerns of configuration management and network provisioning, through the architectural challenges of distributed systems and high-concurrency services, to the emerging frontier of agentic AI. What connects those layers is not the technology but the underlying commitment to systems that are correct, observable, and honest about their failure modes.
My core stack reflects considered tradeoffs, not trend-following. Go handles the performance-critical path: concurrent, fast, explicit about errors. Python owns the data plane and automation layer: expressive, composable, pragmatic. Protocol Buffers enforce the contract between them: typed, language-agnostic, resistant to the drift that kills long-lived systems. Each tool earns its place.