Detection Engineer

We're an Australian success story, now close to 500 people strong, delivering secure, end to end digital solutions across cloud, collaboration, cyber security, data services and network infrastructure, all backed by over 20 years of experience. Trusted by some of Australia's biggest brands, Orro leads the way in designing, building and operating digital infrastructure that delivers greater efficiency, agility, performance and resilience. Our solutions take the stress out of tech for more than 400 businesses and over 20 million Australians every single day.

Our mission? To create "future now" solutions making it faster, simpler and safer for people to access, store and share information, wherever they are and whoever they're with. But more than that, we know that real impact comes from connecting people, not just machines. That's why we take the time to understand our clients; how they work, what matters to them, and where they're headed so we can deliver not just what they need today, but what they'll need next.

With offices in Sydney, Melbourne, Canberra, Brisbane and Perth, and teams across New Zealand, the Philippines and the UK, Orro is known for delivering future ready solutions, backed by deep expertise, genuine human insight and lasting partnerships.

You'll own detection content across the platforms our customers run, whether that is Microsoft Sentinel, SentinelOne or Splunk. A typical week mixes authoring new detections against a freshly mapped technique, tuning down the false positives that consume analyst attention, tracing a coverage gap back to a missing log source, and pairing with the SOC to make sure the content you ship lands well in triage. You'll work closely with threat hunters, SOC analysts and customer stakeholders, both remotely and onsite, and you'll mentor less experienced members of the team along the way.

Design high fidelity detections for subtle or evasive behaviours, balancing coverage against noise and writing logic that ports cleanly across SIEMs

Tune the highest volume rules by finding the root cause of noise in the data, not just the rule, and improve precision systematically