Magnus McCune
CTO
HiveMQ • SF • CDMX • Toronto
Professional Biography
Magnus is CTO at HiveMQ, where he sets technology strategy and leads R&D for new product lines in data intelligence and agentic AI. He came up through architecture and engineering, and that background shapes how he approaches the role: start with the system, not the slide deck.
His career has spanned cloud infrastructure, enterprise platforms, security, and developer tooling before landing in industrial IoT and operational technology. The common thread is distributed systems with messy constraints: legacy integrations, organizational complexity, and the gap between what the architecture diagram promises and what production actually delivers. The current chapter is industrial, but the problems are universal.
Before the CTO role, he spent years translating between technical teams and business outcomes, converting large ambiguous problems into architectures, decisions, and scoped bets teams can execute on. That work took him through hands-on engineering, solution architecture, and product strategy across industries.
Active in MQTT and Sparkplug B standards work through the Eclipse Foundation.
He mentors engineers and architects, mostly on depth vs. breadth decisions, navigating ambiguity, and building a point of view worth defending.
The blog captures experiments, working notes, and field patterns from that work. Expect critical thinking over hype, tradeoffs over silver bullets, and a bias toward showing the failure modes alongside the wins.
What I work on
What I Build
- •Industrial data platforms: the full stack from device connectivity through data modeling to AI-driven action
- •New product incubation: validating ideas, scoping MVPs, running lighthouse programs, deciding what makes it to roadmap
- •Multiplicative teams: design, developer experience, and platform engineering as functions that make the whole org faster
Technical Interests
- •Distributed systems across on-prem, cloud, and edge environments
- •MQTT, event-driven architectures, and operational data at scale
- •Semantic data modeling, knowledge graphs, and industrial ontologies
- •Agent runtimes, orchestration, and governance for real-world environments
- •MLOps and model optimization (ONNX Runtime, TensorRT)
- •Kubernetes, cloud platforms (Azure, AWS, GCP), and infrastructure as code
How I Think
- •In layers: data movement, then modeling and governance, then decision and action
- •Labs first: PoCs and homelab experiments to de-risk before committing
- •Tradeoffs made explicit: security vs. speed, cost vs. flexibility, technical ambition vs. organizational readiness
- •Diagrams over decks: ERDs, sequence diagrams, architecture reviews, long-form writing
Beyond work
He runs an opinionated homelab (edge devices, Jetson hardware, multiple Kubernetes clusters, Proxmox, embedded boards) as a proving ground for ideas before they get near a customer or a roadmap. Beyond tech: cities explored by coffee and tacos, outdoor time for mental reset, and a persistent curiosity about how things are built, from building science to machining to 3D printing.
Connect
I'm always interested in connecting with fellow engineers, discussing technical challenges, or exploring collaboration opportunities.