We are AI-first and will give you the tooling for it. Producing code is now the inexpensive part; the value is in specifying, verifying, and standing behind every number.

• Omni: direct experience is a significant advantage given the migration ahead.
• A regulated environment: payments, banking, EMI, or crypto.
• Payments, FinTech, or digital assets domain knowledge.
• Workflow automation (n8n, Zapier, or similar) and reverse ETL., * a technical and behavioural conversation with the Head of Data including live SQL
• a session with leads from the teams you would work alongside
• a conversation on judgement and reasoning

Data engineering, data science, streaming infrastructure, machine learning, or software engineering., * 4+ years in analytics engineering or a comparable role.

• Degree subject matters far less than how you reason. A numerate or analytical education helps, but no particular discipline is required.

• 30 days holiday, plus 4 wellbeing days and 1 volunteering day
• Salary sacrifice pension scheme
• AXA health insurance
• Income protection
• Life assurance
• Hybrid working (1-2 days in the office)
• Enhanced parental leave
• Salary sacrifice creche and cycle to work scheme

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• dbt on BigQuery, day to day. Model design, data quality, the source-of-truth layer, and every change that lands in it.
• Joining the business together across systems. Our CRM, ledgers, operational tools, and compliance systems each hold part of the same picture, under different keys and different definitions. Resolving them into one coherent model is central to the role.
• Leading our Lightdash to Omni migration, then owning the semantic layer and everything built on it.
• Regulatory and compliance reporting. Recurring submissions and the models behind them, against externally fixed deadlines.
• Partnering with Finance, Product, Operations, Compliance, Sales, and the executive team. Understanding the decision behind each request, not only fulfilling it.
• Quality-of-life automations. Removing manual steps and returning data to the tools teams already use. A steady part of the role rather than a large one.

How the role works

• Lean team, wide remit, high pace: five or six unrelated requests in flight in a typical week, with priorities evolving as you go.
• You will set conventions, definitions, and standards rather than inherit them.
• Briefs arrive short. Establishing what is actually being asked is part of the work.
• You will have daily access to the Head of Data and a structured handover, alongside an expectation that you reach independence quickly.
• Regulatory work is recurring, deadline-driven, and rarely the most creative part of the week. It still has to be right, and on time.
• Senior-level dbt. You have owned a project end to end and made structural decisions you would still defend.
• Senior-level SQL. Complex, clean, and performant, with a clear view of what your queries cost. There is a live SQL exercise in the process.

What matters most

Technical skill gets you into the process. Business understanding is what distinguishes candidates.

• You understand how systems interact commercially, not only structurally: what a product does before you model it, which system is the record of truth when two disagree, and what a number is for.
• You design entities and grain that match how the business thinks, rather than how the source systems happen to store things. Entity resolution across systems is a modelling problem before it is a technical one.
• You think about consequences, trade-offs, incentives, and knock-on effects.
• You start from the decision, not the query.
• You are comfortable being treated as a peer: able to disagree with business leads and with the Head of Data, and to change your view when the argument against you is better.
• You explain your work clearly to people who do not work with data.