Principal Engineer (Hiring Immediately)

Moneyhub empowers leading financial services organisations to understand their clients’ needs better. We provide the data services and embedded technologies required to deliver highly personalised, lifelong financial relationships.

We are a data and payments company building ISO 27001-certified software for Open Banking, Open Finance, and Open Data. Our FCA-regulated platform lets organisations turn financial data into personalised digital experiences and initiate payments and hundreds of organisations across finance, media and retail rely on it.

The Role

We are seeking an experienced engineer who has built things people actually use and still wants to write the code.

You will have taken products from nothing to real users, more than once and seen what happens to them afterwards. We will ask what you shipped, who used it and what you did yourself. We mean end users, not just other engineers.

Scale and performance matter here - clients notice when things are slow. You know how to find the bottleneck rather than guess at it. You are comfortable reading a flame chart and you will follow a problem down as far as it goes, whether that is a React component re-rendering because of a misused useEffect or cache locality in the hot path of an algorithm.

You will have seen approaches fail in different companies and codebases and you know why. People will want your view before they start something and you will have influenced what a team builds without being their manager.

You also need to be using agentic engineering already and treating it as a multiplier. One good engineer can now take on work that used to need a team. We want to hear your opinions and your approach.

What you would work on

In your first 6–12 months, you might:

• Ship a committed piece of work to production in your first three months, and come out of it with two or three platform improvements you think we should take on next.
• Take a project of your own from prototype to production, such as redesigning part of an event processing or data pipeline so it handles more load, more reliably.
• Set the standard for agentic engineering: show the team the workflows that work for you, and turn them into patterns other teams adopt.

Success in this role looks like a steady record of shipped work, with at least two of your architectural patterns or AI standards in use across engineering.

How you would work

You would join our experienced engineering team - we are about 40 software/platform/data engineers and data scientists. We use Shape Up, and you would work within it: shaping bets, delivering committed work, and getting things over the line with the teams.

Our culture is based on trust, and we empower people to deliver in the way that suits them best. For this role, that means real say over how you solve problems and some latitude over what you pick up, alongside the expectation that your work is visible, shared with your colleagues and pointed at what the business needs. In practice, you might have a committed bet, something you are improving because it needs it, and something you are exploring for a future cycle. Judging which deserves your time, and being open about it, is a big part of the job.

This is an individual contributor role. No line management, and no moving away from the code.

Our Stack

Most of our platform is TypeScript on Node. Python is used across data science and machine learning. We use Kafka for events, Postgres for most of our data, and Kubernetes on AWS to run it all.

We want someone comfortable across data science, Python, Node, React and Rust. You do not need to be an expert in each, but none of them should put you off. If you have not used a language before, we expect you to pick it up.

Requirements

What we’re looking for

• Experience at several startups and on systems at real scale, shipping software that people actually used
• A track record of building and running software in production across different companies and codebases, including the unglamorous years after launch
• The person others turn to when something is hard or has gone wrong, with experience setting technical direction without authority over the people doing the work
• A habit of finding where a system slows down under real load , working out why and fixing it, using profilers and flame charts rather than guesswork. Equally at home with a front-end rendering problem or the memory behaviour of a tight loop
• Comfort across Python, Node, React, Rust and data science . Not an expert in each, but none of them a worry
• The ability to work properly with data scientists. No need to be one, but an understanding of how to jud]]>