Testifize
Tech Lead / Senior Software Engineer - Optimizely Platform

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About the role
Testifize is a measurement and experimentation consultancy. We are hiring a senior engineer to lead the technical side of a long-running client platform engagement: embedded alongside the client's product and engineering teams, owning how experimentation and personalisation are delivered across a high-traffic retail web estate.
This is a tech-lead role, not a ticket-taking one. You will own how experimentation sits inside the delivery stack: the feature-flagging platform, the edge and caching layer, the content platform and the customer-data integrations that feed audience targeting. You will be the person product managers and analysts come to when an experiment is not behaving. Expect to spend as much time explaining, reviewing and pushing back as you do writing code.
We are hiring for communication first. The person we want can explain a caching problem to a product manager, argue a design choice in an architecture forum without making enemies, and write a clear Teams message at 5pm on a Friday about why an experiment has to be restarted. If you are technically brilliant but prefer to work heads-down and hand over at the end, this is not the right fit.
What you'll be doing
- Own the feature-experimentation platform implementation across web (server-side and client-side SDKs): decision calls, user-ID strategy, attribute and audience plumbing, event tracking into the analytics stack.
- Lead delivery and ongoing support of edge-side experimentation, including the caching strategy for experiments on server-rendered, CDN-cached pages, variant injection at the edge, and the deployment workflow around it.
- Own the integrations that let non-engineers run experiments: a content-first resolution model with the CMS, audience segmentation fed from the customer-data platform, and the no-code path for editors and PMs to launch tests without a deployment per experiment.
- Run technical investigations when experiments misbehave: sample-ratio mismatches, flicker, consent-timing gaps, redirect and bucketing loss, tag-propagation differences between arms. Find the root cause, fix it or specify the fix, and write it up so the analysts and PMs understand it.
- Review experiment code from product teams before launch and after. Catch wrong activation points, wrong activation scope and missing tracking before they cost a restart.
- Represent experimentation in engineering forums: design authority submissions, architecture reviews, security reviews, long-term support models for the edge layer.
- Maintain a debugging knowledge base and an onboarding path for engineers new to experimentation, so quality holds as more teams ship behind flags.
- Work day-to-day with our experimentation lead and two embedded analysts, and with the client's product managers, web platform engineers and analytics team.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
What we're looking for
- Excellent written and spoken communication. You can explain a technical constraint to a non-technical stakeholder in three sentences, and you write things down.
- Hands-on production experience with a flag-based experimentation platform (Optimizely Feature Experimentation, LaunchDarkly, Statsig, Split or similar) at scale: SDK integration, decisioning, user-ID and bucketing design, datafile and caching behaviour.
- Strong JavaScript/TypeScript and Node.js, with real experience of SSR, CDN caching and edge compute (Akamai, Cloudflare or Fastly).
- Working knowledge of how A/B tests are measured: what an SRM is, what causes one, and why consent timing and redirects break denominators. You do not need to be a statistician, but you need to understand what the analysts are asking for.
- Experience integrating experimentation with at least one of: a headless CMS, a customer-data platform or audience engine, or a tag-management / analytics pipeline.
- Comfortable operating inside a large enterprise engineering organisation: architecture reviews, security sign-off, change control, multiple product teams with competing priorities.
- Senior enough to hold a position under pressure and generous enough to change it when someone shows you a better one.
- Prior consultancy or embedded-contractor experience: you know how to be useful in someone else's organisation without waiting to be told.


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Nice to have
- Experience building self-serve or no-code experimentation tooling for content editors or product managers.
- Exposure to native app experimentation (iOS/Android SDKs) and cross-platform user-ID models.
- Familiarity with first-party event pipelines and consent management, and their effect on measurement.
- Optimizely Web Experimentation alongside a flag-based platform, and a clear view of where each belongs.
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