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Analytics Engineering Manager, FinCrime

AI Summary
Monzo is seeking a hands-on Analytics Engineering Manager to lead its FinCrime analytics engineering team and strengthen data capabilities that protect customers and the business from financial crime. The manager will coach and develop five to six analytics engineers while contributing directly to architecture, code review, pipeline development, hiring, and technical strategy. The role focuses on scalable data-warehouse models, data governance, delivery prioritisation, and cross-functional partnership across Monzo collectives. Experience with large-scale warehousing, ETL or big-data initiatives is essential, while dbt, BigQuery, Looker, and regulated-industry experience are advantageous. The position includes occasional out-of-hours on-call support for owned pipelines.
Remote from
UK
Salary
GBP 97,800–125k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
9 Dec 2026
Listing views
45
Application actions
4
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Opportunity details

About this role.

🚀 We’re on a mission to make money work for everyone.

We’re waving goodbye to the complicated and confusing ways of traditional banking.

After starting as a prepaid card, our product offering has grown a lot in the last 10 years in the UK. As well as personal and business bank accounts, we offer joint accounts, accounts for 16-17 year olds, a free kids account and credit cards in the UK, with more exciting things to come beyond. Our UK customers can also save, invest and combine their pensions with us.

With our hot coral cards and get-paid-early feature, combined with financial education on social media and our award winning customer service, we have a long history of creating magical moments for our customers!

We’re not about selling products – we want to solve problems and change lives through Monzo ❤️


Analytics Engineering Manager – Fincrime

London/Cardiff/UK Remote | 💰£97,800-£125,000 + Incentive Awards tied to your performance and benefits | Hear from the team ✨

⭐Our Fincrime Analytics Engineering Team

Our Financial Crime Collective helps keep Monzo and our customers safe. We build effective, proportionate controls to protect against financial crime while delivering great customer outcomes.

Our Analytics Engineering discipline works in the intersection between data, engineering and our collectives – Core, Money, Borrowing, Operations and Financial Crime and beyond.

The AE teams are responsible for building downstream data models from backend services with the desire to make our Data Warehouse a genuine competitive advantage for Monzo. We want a discipline capable of building a reliable and scalable Data Warehouse to support decision making in a cost effective and performant manner.

Managers within Data have two primary responsibilities; people and technical products. Your focus will be on helping engineers with their personal and professional development, listening and guiding them through hard times and celebrating their successes. You will also be leading and participating directly in technical initiatives and helping Monzo shape its Data organisation, ensuring the team focuses on valuable work, shipping things with a level of care and attention to detail.

There will be a strong focus on delivering best practice across our data discipline and helping bring a new level of maturity around Data Governance principles, working in collaboration with others to deliver this.

🔑You’ll play a key role by…

Working in a multi-disciplinary team, you will:

  • Be a hands-on leader in building a discipline of exceptional analytics engineers working to make Data at Monzo the gold standard within the industry
  • Nurture between 5 to 6 analytics engineers, supporting, coaching and developing high performing engineers through regular 1:1s, continuous feedback and relationships with others
  • Aid prioritisation of initiatives and projects, working closely with other leads for each of our Monzo collectives
  • Be hands on through participating in the review cycle, architecture and design leadership and development of your own changes to the pipelines
  • Be part of the hiring team within Analytics Engineering
  • Work closely with other leads to deliver a scalable, consistent approach to governance and best practices
  • Drive effective project management of central Analytics Engineering projects, ensuring they’re well scoped and delivered to deadlines
  • Establish yourself as a trusted partner to various collectives and the leadership team, with the capacity for getting things done, be it either hands-on or by leading others
  • We all own and support the pipelines we contribute to, and on call support out of hours will be expected from time to time as part of this role

🤩We’d love to hear from you if…

You have experience managing or mentoring the performance and development of high-performing engineers.

  • You have strong experience in data warehousing architecture at scale
  • You have experience and a passion for leading data warehousing, data visualisation, big data or ETL projects as an analyst, developer, designer or architect
  • You know what it takes to hire great engineers within the data space
  • You’re equally comfortable working hands on and leading a team

Nice To Haves:

  • Any experience working within a finance or banking environment
  • Experience working in a highly regulated environment (e.g. finance, insurance, gaming, food, health care)
  • Experience with any of our stack of dbt, BigQuery and Looker

Not ticking every box? That’s totally okay! Studies show that women and people of colour might hesitate to apply unless they meet every single requirement. At Monzo, we’re dedicated to creating a diverse and welcoming team. If you’re passionate about this role and keen to learn and grow with us, we encourage you to apply— even if you don’t have everything that’s listed just yet. Drop us your application, we’d love to hear from you!

🙌What’s in it for you

💰£97,800-£125,000 + Incentive Awards tied to your performance and benefits

✈️ We’ll help you relocate to the UK.

✅ We can sponsor your visa.

📍This role can be based in our London office, but we’re open to distributed working within the UK (with ad hoc meetings in London)

⏰We offer flexible working hours and trust you to work enough hours to do your job well, and at times that suit you and your team.

📚£1,000 learning budget each year to use on books, training courses and conferences.

🏡We will set you up to work from home; all employees are given Macbooks and for fully remote workers we will provide extra support for your work-from-home setup.

➕ Plus lots more! Read our full list of benefits.

🌈 The application journey has 4 key steps

  1. Recruiter Screening Call
  2. Initial Call
  3. Take Home Task
  4. Final Loop (3 sessions – case study, collaboration and people leadership)

This process should take around 2-3 weeks – your schedule is really important to us, so we promise to be as flexible as possible!

We have some guidelines on using Artificial Intelligence (AI) to ace an application and interview at Monzo. You can read them here.

For more insight into the AE team at Monzo, check out our podcast with John Azzopardi one of our Senior Leaders in AE

#LI-AS1 #LI-REMOTE


Equal opportunities for everyone

Diversity and inclusion are a priority for us and we’re making sure we have lots of support for all of our people to grow at Monzo. At Monzo, we’re embracing diversity by fostering an inclusive environment for all people to do the best work of their lives with us. This is integral to our mission of making money work for everyone. You can read more in our blog, 2026 Diversity and Inclusion Report and 2025 Gender Pay Gap Report.

We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion, sex, sexual orientation, gender identity, family or parental status, national origin, or veteran, neurodiversity or disability status.

If you have a preferred name, please use it to apply. We don’t need full or birth names at application stage 😊

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior people-and-technical leadership role requiring strong data-warehouse architecture expertise alongside coaching, hiring, governance, and delivery accountability. Financial-crime context, platform reliability expectations, and hands-on engineering involvement add substantial complexity.

Salary analysis

Estimated compensation compared with the broader UK market for similar roles.

Estimated job median (per year)Market rate
£111,400
UK market range (per year)£95k–£140k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base-pay range of £97,800–£125,000, with incentive awards listed separately. The annual cadence is inferred from the senior full-time UK role and the magnitude of the stated range. A reasonable estimated UK market range for an analytics engineering manager with financial-services and data-platform leadership responsibilities is approximately £95,000–£140,000 per year; this is an AI estimate, not verified salary research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you balance people leadership with remaining hands-on in analytics engineering?

I would set clear ownership boundaries, reserve regular time for architecture reviews and selected high-leverage implementation work, and delegate delivery ownership to senior engineers. Regular one-to-ones, coaching plans, and transparent team priorities would ensure that hands-on work supports rather than displaces management responsibilities.

How would you improve reliability and trust in a large-scale data warehouse used for financial-crime decisions?

I would begin by defining critical data products, ownership, quality expectations, lineage, and service-level objectives. I would then implement automated testing, freshness and volume monitoring, documented contracts, incident processes, and staged deployment practices so issues are detected early and resolved with clear accountability.

Describe how you would prioritise competing analytics engineering requests from multiple business collectives.

I would assess requests against customer and regulatory risk, strategic impact, urgency, dependencies, effort, and operational cost. A visible prioritisation framework and regular stakeholder reviews would make trade-offs explicit, while preserving capacity for platform health, technical debt, and unplanned incidents.

What is your approach to developing a team of five to six analytics engineers?

I would establish individual growth plans based on career goals and demonstrated strengths, provide frequent actionable feedback, and create opportunities for ownership of technical initiatives. I would also use calibration, pairing, design reviews, and measurable expectations to build consistency and psychological safety.

How would you introduce stronger data-governance practices without unnecessarily slowing delivery?

I would make governance practical and embedded in engineering workflows rather than relying on manual approvals. Lightweight standards for ownership, naming, documentation, access, testing, and lineage—supported by templates and automation—can improve control while keeping teams productive.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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