Cloud Efficiency Engineer (AWS/FinOps)

Every build, test, and deployment our customers run costs us something. Right now we can see the top-line number — our AWS and Datadog spend — and we can see revenue. What we can't see clearly enough is the line between them: which operations drive our cost of goods sold, how that changes as customers scale, and which changes would move it. If you’ve been in a similar role but have been frustrated by not being able to go super deep on this and model different categories/scenarios and costs then you’ll be able to scratch that itch here.

Cost questions arrive from the exec team as business questions, and today the answers get assembled part-time by people whose main job is something else. This role is designed to close this gap.

This is a hands-on engineering role, not a reporting one: you'll own Buildkite's cloud efficiency picture end to end — tracing COGS signals down to the tight loop technical drivers that cause them, building the models that explain them, then shipping or driving the changes that bring them down, and proving afterwards that the saving actually landed. You'll report to the VP Engineering Platform and pair closely with him for your first month or two before running with substantial autonomy. You'll also partner with our CTO, Platform engineering, with the Data team who own the canonical COGS model, and with Finance on the FinOps side. The constraint that makes this interesting is that none of this work can come at the expense of reliability, throughput, or customer experience. Ultimately this role will help us with cost optimisation and directly driving growth through technical efficiency.

Cutting the bill is easy. But cutting the bill without anyone noticing except the CFO is basically the crux of the job.

What You'll Do

Follow the money

Trace cost signals to their technical causes — down to customer-level job costs & state machine changes, GraphQL cost by function, and per-operation unit economics — rather than stopping at service-level totals.

Build and maintain cost models for our most common and most expensive operations, so that when the shape of our traffic changes, we can explain what it does to our margin before the invoice arrives.

Rank the opportunities into a defensible portfolio: expected saving, risk, effort, and who has to be involved.

Ship the fix

Implement safe, measurable optimisations yourself where the change sits in code or configuration you can own.

Partner with the engineering teams who own affected systems on the larger work, bringing them evidence rather than an edict.

Drive Datadog and observability efficiency alongside AWS — the second-biggest lever, and often the faster one.

Prove it landed

Establish a credible baseline before every change and verify realised savings after it, in production, with numbers you'll stand behind in front of the exec team.

Build guardrails and tooling — attribution, alerting, and cost visibility — so regressions get caught by a system rather than by a quarterly surprise.

Translate between engineering and Finance in both directions, so the questions Finance asks get technical answers and the changes engineering makes get financial ones.

This Is You If...

You've delivered meaningful, measured AWS savings in a scaled production environment — and you can walk us through one in detail: the baseline, the diagnosis, what you changed, the trade-offs you accepted, and the verified result.

You're a working engineer. You read code, write code, and ship changes; you're not handing findings over a wall for someone else to implement.

You're strong at systems diagnosis — you can take an ambiguous signal ("storage costs jumped 30% last month") and work it down to a precise cause.

You have sound trade-off judgement and you use it. You know which savings aren't worth the reliability risk, and you say so.

You can influence teams you don't manage, because the change that saves the most money will almost always live in someone else's system.

You stand behind your numbers. Whatever methodology you use — and we fully expect you to use AI heavily here — you know how to go from a result of something produced to proof that it's actually true.

Bonus Points If...

None of these are required — bring some, learn the rest here.

You've done Datadog or observability cost optimisation specifically.

You have FinOps engineering experience, or have worked closely with a finance function on unit economics and cost attribution.

You've built granular cost attribution or automated cost guardrails from scratch.

You've worked with GraphQL at scale, or on developer tooling, CI/CD, or cloud infrastructure products.

You come from a scaled SaaS, platform, or observability organisation with a material AWS footprint.

You've got a finance or commercial background alongside the engineering one — this role rewards being fluent in both.

We're deliberately open on background here. If you haven't held a cloud efficiency or FinOps title before but this problem is the one you'd most like to be handed, apply.

At Buildkite, we value diversity and celebrate all types of skills, backgrounds, and experiences. We’re dedicated to fostering an inclusive environment and providing reasonable accommodations throughout our recruitment process.

If you need any accommodations or support during the application or interview process, please reach out to us at accommodations@buildkite.com.