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Staff Software Engineer

CompraTica Empleos

EMP:Technology
London
Tiempo Completo
Remoto
0 vistas

Descripción

Company DescriptionWe’re Checkout.

You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day.

We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers.

Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes.

Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale.

If you want to do career-defining work, you’ve come to the right place.

We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact.

With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started.

As a Staff Engineer at Checkout.

com, you will set the technical direction for systems that process payments at very high throughput, where correctness matters on every single transaction and has significant impact.

This is a role for engineers who are energised by scale and by the hard trade-offs that come with it: latency budgets, failure domains, consistency guarantees, and the operational reality of running critical infrastructure around the clock.

You will operate as a force multiplier across multiple teams - shaping architecture, raising the engineering bar, and turning ambiguous, business-critical problems into designs that can be delivered incrementally.

We're looking for individuals with deep technical judgement, strong ownership, and a pragmatic approach to breaking big problems into smaller iterations that deliver value continuously.

How you'll make an impactOwn the architecture of high-throughput, low-latency distributed systems, defining the service boundaries, data models and integration patterns that let them scale by an order of.

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