Owning an engagement area: Taking long-term accountability for one of Canva's highest-risk technical domains, sharding core data stores, resource utilisation, distributed systems challenges at scale embedded alongside the team that owns it
Writing production software: The work is code, not process. Instrumenting, refactoring, rebuilding the pieces that cause problems at scale. You're a software engineer first; the reliability outcome at scale is what you're optimising for
Collaboration: Opportunity to pair, mentor and learn from fellow production engineers
Customer First: Striving for fewer incidents, faster recovery, lower severity, latency that bends in the right direction. Taking pride in moving needle metrics, that positively impacts the quality of the customer experience
Platform contributions: Where you see a pattern that needs a shared capability, you bring it back, not to own it indefinitely, but to seed the platform work that scales beyond your engagement
Compounding at the system layer: One engineer who truly understands a system can change how every other engineer builds on top of it. That's the leverage in this role and why the archetype matters more than the domain
What success looks like: As a secondee, developing trusted relationship with your team. Guiding them towards shipping at velocity, with more confidence and less toil
You’re probably a match
We'd love to hear from you if you fit one or more of these. You don't need to meet all of them, but the more the better and if you join the team, we're invested in helping you grow. Experience
Production at scale: Owned reliability work within large-scale distributed systems. When things broke, you wrote the fix, not the ticket
Embedded delivery: Previously worked as an engineer embedded in or partnering closely with a product or feature team, not siloed in a platform org that throws tools over the fence
JVM or systems depth: You've built real things in Java, Go, Rust, C++, or a comparable systems language at production scale; commercial depth, not academic familiarity. We're language-flexible for the right engineer, but you need to show up and win the technical duel in the first meeting
Distributed systems in practice: Navigated sharding, replication, failure modes, consistency tradeoffs in real systems
Debugging large codebases: Ability to parachute into an unfamiliar codebase, orient quickly, find where the problem actually lives, and fix it
Influence without authority: Proven to have made things better in systems through wisdom and trust
Networking Depth: You know the network stack and what traffic looks like a scale
Linux internals: Enough kernel-level understanding to reason about what's actually happening when a system misbehaves process scheduling, memory, I/O, network stack
Distributed systems patterns: Consistent hashing, leader election, consensus, backpressure, circuit breakers
Observability tooling: You've instrumented systems for real, built the tracing, the dashboards, the alerting that actually tells you what's wrong. You understand the difference between causal and symtom based alerting and know what a good SLO looks like
Containerisation and orchestration: Kubernetes at production scale, you understand what happens at the scheduler level
Performance analysis: You've profiled JVM applications or systems-level processes, found the thing nobody was looking at, and fixed it in a way that lasted
Cloud infrastructure: AWS at meaningful depth, so you understand how they behave under load and at the edges
Incident response in practice: You've been on-call in a serious production environment and have opinions about what good incident management actually looks like