Staff / Principal Data Engineer, Platform

Hybrid, typically 2 to 3 days per week onsite

Shape the data architecture behind the next generation of AI

The next generation of AI won't be built on static datasets alone. It will learn through active experience.

They're a fast-growing AI infrastructure company building the foundations for this shift: interactive, resettable digital environments where AI agents can navigate software, complete tasks, learn from failure and improve through trial and error. They have engineering teams in London and Paris.

Delivering those environments creates real engineering challenges around how data is acquired, processed, secured, evaluated and made available across the platform.

They're looking for a Staff or Principal Data Engineer to help define how we solve those problems as we scale. This is a deeply hands-on individual-contributor role within Platform. You'll combine strong software and data engineering with architectural leadership, helping shape an important technical area while continuing to build.

What you'll be doing

• Own the technical direction for significant areas of our data infrastructure
• Take ambiguous data problems and turn them into clear architectures, systems and execution plans
• Design scalable approaches to acquiring, ingesting, processing and integrating large datasets
• Build and evolve infrastructure for data quality, sanitisation, security and privacy
• Make architectural decisions around storage, processing, reliability, observability, scalability and cost
• Identify where existing approaches will stop working and lead the move towards better ones
• Establish patterns and systems that other engineers can build on, rather than solving each dataset independently
• Work across Data, Platform, Software and AI Engineering boundaries to solve end-to-end problems
• Provide technical direction to other engineers without needing formal line-management authority
• Raise the engineering bar through design reviews, technical judgement and hands-on contribution
• Stay close to code, production systems and debugging rather than becoming a purely architectural role
• Use modern AI tools actively while applying strong engineering judgement to their output

At this level, success isn't simply delivering a pipeline. You should be able to recognise the underlying technical problem, determine the right direction and create leverage for the wider engineering organisation.

What we're looking for

You're an experienced technical leader who has spent significant time building data-intensive production systems, and you've developed strong opinions about architecture because you've seen different approaches work, fail and evolve in real environments.

You’ll likely have:

• Deep software and data-engineering fundamentals
• A strong record of owning complex production systems end to end
• Experience defining architecture across multiple systems or significant technical areas
• Evidence of leading major technical work while remaining an individual contributor
• Experience working with large, complex or heterogeneous datasets
• Strong judgement around distributed processing, storage, reliability and operational complexity
• Experience making consequential technical trade-offs rather than simply adopting established patterns
• The ability to explain why an architecture was chosen, what failed and what you'd now do differently
• Experience influencing and coordinating engineers beyond your own individual implementation work
• Strong debugging and production instincts
• High levels of initiative and comfort operating with incomplete information

They're deliberately stack-agnostic. Your ability to reason about systems and architecture matters considerably more than whether your previous company happened to use our technologies.

We'd be particularly excited if you have

• Designed or significantly evolved large-scale data platforms
• Worked in a highly data-mature engineering organisation
• Experience with distributed systems and large-scale processing
• Worked extensively with unstructured or semi-structured datasets
• Seen multiple generations of data architecture and led meaningful migrations between them
• Experience with data acquisition, ingestion and dataset lifecycle at significant scale
• Experience with privacy, anonymisation, PII detection or secure data processing
• Built infrastructure supporting ML, AI or research workloads
• Helped establish engineering standards or technical direction across a team or organisation
• Worked in both mature engineering environments and faster-moving startup or scale-up settings

Previous AI experience is useful but not required. We're more interested in your technical judgement, curiosity and your ability to apply what you've learned from complex data systems to a new generation of AI infrastructure.