Let’s be honest: a few hours of interviews can’t fully capture years of experience. And interviewing is a skill in itself. Not everyone’s had the chance to practise recently, or even get useful feedback. If you’re feeling unsure where to start, you’re not alone.

This guide is here to help you understand what to expect and how to approach the Analytics Engineering (AE) interview process at Monzo—so you can feel prepared, confident, and able to show your best work.

At Monzo, we try to be transparent in everything we do—including hiring. We actively try to set candidates up for success. Our interviews are designed to help us understand your strengths, not catch you out.

As you go through the process, we’re really trying to answer two questions together:

We’re excited by people who are curious, proactive, and motivated to make an impact—whether that’s through big ideas or thoughtful execution.

Analytics Engineering at Monzo sits at the intersection of Data Engineering, Data Science, and Product.

We work in a hub-and-spoke model:

This means Analytics Engineers are deeply involved in product development—partnering closely with engineers, data scientists, and stakeholders to shape how data is modelled and used.

In practice, this often looks like:

A big part of the role is bridging the gap between raw data and meaningful insight—making sure the data is not just available, but usable.

Our current stack includes tools like DBT, BigQuery, Looker, SQL, and some Python—but more important than specific tools is how you think about modelling, transformation, and usability.

Our Analytics Engineering interview loop looks like this:

Here’s how to approach each stage.

There’s no need to prep heavily for this.

It’s an exploratory conversation to understand whether there’s a good match on both sides. Think of it as a two-way conversation, not an assessment.

This is where we start to go deeper. Come prepared with one or two projects you’ve worked on end-to-end, and be ready to talk about:

It’s especially helpful if your examples include:

One important thing: we’re interviewing you, not your team. Be clear about what you did, and most importantly—focus on why.

This stage reflects the kind of work you’d actually do as an Analytics Engineer at Monzo. Strong submissions are clear, well-reasoned, and pragmatic—not over-engineered.

We’re less interested in perfection, and more interested in:

This is a collaborative discussion, not a test you need to “finish”.

We’re much more interested in how you think than whether you land on a perfect answer.

A strong approach usually looks like:

We’ll explore how you work with others, make decisions, and grow over time. This is where we talk through your experience as part of a team, working with other engineers, breaking down complex work and delivering on projects. A core part of our day to day is working alongside our team and the broader company to deliver to large scale goals incrementally and at scale.

Strong answers usually:

Examples where things didn’t go perfectly—and what you did next—are often the most insightful.

Interviewing is a two-way street. This process isn’t just about whether you’re a good fit for Monzo, it’s also about whether Monzo is the right place for you.

We’re not looking for perfect answers—we’re looking for thoughtful people who care about impact, collaboration, and building things well. Come ready to tell your story, ask questions, and be curious about ours.

We’re looking forward to getting to know you.