The Opportunity
Enterprise data platforms in financial services have never been more demanding to build, govern, or scale. Regulatory expectations are rising, use cases are diversifying — from traditional warehousing and reporting through to AI and NLP-driven analytics — and the pressure to deliver high-quality, auditable, decision-grade data has never been greater.
This is a senior individual contributor role for someone who operates comfortably across the full techno-functional spectrum: hands-on with data, fluent in architecture, and credible with both engineering teams and business stakeholders. You will work across a deliberately broad remit — multiple domains, multiple data types, multiple technology layers — and be expected to bring structure and clarity to complex, ambiguous problems.
If you have built your career solving hard data problems in large, regulated environments and you are looking for a role with genuine scope, this is worth your attention.
What You Will Be Doing
- Taking end-to-end accountability for data and analytics solutions: understanding the business problem, shaping the design, and seeing it through to delivery and support.
- Working across a wide range of data domains — risk, finance, compliance, payments, products, and more — rather than within a single vertical.
- Designing and governing data architectures that span both structured and unstructured data, across warehouse and lake environments.
- Getting into the data directly — querying, profiling, and making sense of large, complex datasets to validate designs and support analytical outcomes.
- Engaging non-technical stakeholders with confidence, translating between what the business needs and what the technology can deliver.
- Supporting analytics use cases that increasingly involve AI, ML, and NLP — understanding how these outputs need to be governed, explained, and made fit for regulated consumption.
- Contributing to platform evaluations, proof-of-concept exercises, and broader modernisation efforts as the organisation continues to evolve its data capability.
What You Will Bring
- Around 8 to 12 years working in data analytics or system analysis roles, with substantial time spent inside large financial institutions or equivalently complex regulated environments.
- Real experience with enterprise data warehouse and data lake platforms at scale — the kind that comes from having been through at least one or two full implementation cycles in a banking context.
- Strong, working SQL skills and Python proficiency (Pandas, NumPy) — these are non-negotiable foundations for this role.
- Familiarity with modern data platforms and the broader technology landscape: cloud warehouses, distributed processing engines, open table formats, data integration tooling, and visualisation layers.
- Enough exposure to AI, ML, and NLP concepts to engage meaningfully with data science teams and contribute to the design and governance of AI-enabled analytical use cases.
- Domain experience in one or more of the following: financial crime analytics, credit and statistical scoring, finance and risk reporting, compliance, or wholesale and retail data — you won't be expected to cover all of these, but depth in some and curiosity about the rest matters.
- A genuinely broad, adaptable profile — this organisation values fungibility, and the role is structured to develop it.
Candidates from non-traditional backgrounds are encouraged to apply. Full-stack developers who have gravitated toward data and analytics, or professionals who have built deep data platform experience through system integrator or consulting environments, will find the scope and culture of this role a strong fit.
