Cloud Cost Optimisation Engineer

Role overview

This hands-on engineering role owns cloud cost efficiency end to end for a CI/CD platform that powers builds, tests, and deployments for engineering teams worldwide. The position exists to close the gap between top-line cloud spend and revenue by tracing cost-of-goods-sold signals down to their tight-loop technical drivers, building the models that explain them, and shipping the changes that bring them down without sacrificing reliability, throughput, or customer experience.

Responsibilities

- Trace cost signals to their technical causes, from service-level totals down to per-customer job costs, state machine changes, GraphQL cost by function, and per-operation unit economics. - Build and maintain cost models for the most common and most expensive operations, so traffic shifts can be explained in margin terms before invoices arrive. - Rank optimisation opportunities into a defensible portfolio weighing expected saving, risk, effort, and who needs to be involved. - Implement safe, measurable optimisations directly in code or configuration where ownership allows, and partner with engineering teams on larger changes with evidence rather than directives. - Drive observability and monitoring-tool efficiency alongside cloud spend, treating it as a major and often faster lever. - Establish credible baselines before each change and verify realised savings in production afterwards, building attribution, alerting, and visibility tooling so regressions surface as system signals rather than quarterly surprises.

Requirements

- Demonstrated record of delivering meaningful, measured AWS savings in a scaled production environment, with the ability to walk through one example in detail: baseline, diagnosis, change, trade-offs accepted, verified result. - Strong systems diagnosis skills, able to take an ambiguous signal such as a sudden storage cost spike and work it down to a precise cause. - A working engineer who reads, writes, and ships code rather than handing findings over a wall for someone else to implement. - Sound trade-off judgement about which savings are not worth the reliability risk, and the willingness to say so. - Experience translating between engineering and finance in both directions, or working closely with a finance function on unit economics and cost attribution.

Nice to have

- Datadog or observability cost optimisation experience specifically. - Background building granular cost attribution or automated cost guardrails from scratch. - Familiarity with GraphQL at scale, developer tooling, CI/CD, or cloud infrastructure products. - Prior work at a scaled SaaS, platform, or observability organisation with a material AWS footprint. - A finance or commercial background alongside the engineering one.

Benefits and work setup

- Fully remote position, with the ability to work from anywhere in the world for up to six weeks per year on top of the regular remote setup. - Twenty days paid annual leave plus ten days sick and carer's leave. - Sixteen weeks paid parental leave for primary carers and six weeks for secondary carers. - A remote-work allowance for home-office kit, co-working, or alternative workspaces. - Confidential employee assistance programme and mental health support. - Time and budget for conferences, courses, and coaching to support learning and growth. - Retirement and health coverage, with specifics varying by region.

The role is fully remote but requires residency in either the ANZ or PST timezone.