Data Architect

Finova is the UK’s largest financial services technology provider, supporting one in every five mortgages nationwide. Our agile, cloud‑native solutions enable over 60 banks, building societies, specialist lenders, equity release providers and a network of 2,400+ brokers to stay ahead in a competitive market.

Built on open architecture and backed by deep industry expertise, our platform is designed to scale. Each year, we process over £50 billion in loans, manage nearly £50 billion in savings, and support the digital servicing of more than 650,000 borrower accounts.

Be part of a team that’s driving innovation, enabling growth, and shaping the future of UK lending.

We’re looking for a Data Architect to define and shape the data foundations enabling AI‑driven credit decisioning and the next generation of our data platform. This role exists to bring structure, coherence and future‑proofed thinking to how data is organised, accessed and leveraged across Finova’s mortgage decisioning ecosystem.

Over the next 12 months, success will be measured by the clarity of the canonical data model, the robustness of data pipelines supporting rapid AI experimentation, and the alignment of our decisioning data platform with longer‑term enterprise strategy. This is a highly collaborative role, partnering across Product, Engineering and Data Science to ensure data becomes a governed, reusable and commercially valuable asset.

Defining Data Strategy (with Pragmatism)

• →Shape the data architecture for AI‑driven credit decisioning, balancing speed to market with long‑term scalability.
• →Set clear principles, standards and guardrails for data ingestion, storage and access.
• →Make pragmatic trade‑offs between short‑term delivery pressures and long‑term enterprise alignment.
• →Provide architectural clarity and direction that accelerates delivery without compromising future flexibility.

• Develop and evolve a canonical data model across key mortgage entities (e.g., Applicant, Property, Loan, Broker, Decision).
• Standardise and rationalise data from disparate sources (Azure SQL, SQL Server, Blob Storage, external APIs).
• Ensure models support ML feature engineering, explainability, analytics and operational decisioning.
• Introduce naming conventions, lineage patterns and reference data structures to drive consistency.

• Partner with Data Science to ensure data is fit for modelling, prediction, explainability and monitoring.
• Define data structures that support feature reuse, lineage, auditability and regulatory compliance.
• Ensure data foundations support explainable and traceable decisioning workflows.
• Embed governance practices that support high‑stakes, regulated environments.

You’ll play a key role in shaping the organisation’s data capability alongside the Director of Engineering and Director of Data:

• Work closely with Product to translate business problems into concrete data requirements.
• Contribute to the development of the enterprise data strategy and ensure decisioning data aligns with it.
• Act as a bridge between strategy and execution, grounding decisions in practical delivery experience.
• Partner with Engineering teams to design scalable, implementable pipelines and data services.
• Support Data Science in accessing and shaping high‑quality training and validation datasets.
• Make complex data concepts accessible to both technical and non‑technical stakeholders.

• Take a hands‑on approach to shaping and refining data solutions.
• Unblock teams quickly by making decisive architectural calls.
• Promote reuse of data assets, avoiding duplication and fragmentation.
• Ensure documentation, metadata, lineage and governance remain up to date and adopted.

You will be a strong fit if you bring:

• Proven experience designing data architectures and data models in complex environments.
• Strong understanding of data pipelines, ETL/ELT and cloud‑native platforms.
• Experience with both operational and analytical datasets, ideally in regulated sectors such as financial services.
• A track record of guiding multidisciplinary teams through data‑related decisions.

• Strong SQL skills and experience defining data models, transformations and performance‑optimised structures.
• Understanding of batch, streaming and ELT/ETL processing patterns.
• Experience with Azure, AWS or GCP data platforms (data lakes, warehouses, orchestration, compute).
• Familiarity with modern data architectures (lakehouse, medallion, warehouse).
• Desirable: experience with ML‑related data processes (feature stores, training pipelines, monitoring).

• Commercial acumen — able to weigh cost, value, risk and time‑to‑market.
• Bias for action — moves from ambiguity to clarity quickly.
• Pragmatic, outcome‑focused thinker — avoids over‑engineering.
• Curious, experimental and interested in AI/ML innovation.
• Collaborative by default — thrives in cross‑functional environments.
• Clear communicator — able to explain data concepts simply and confidently.

• A clear, usable canonical data model unifying fragmented mortgage datasets.
• Data pipelines that enable rapid experimentation and AI model development.
• Reduced friction between Product, Data Science and Engineering teams.
• A scalable data foundation supporting both MVP delivery and future enterprise expansion.
• Balanced, thoughtful architectural decisions that maintain delivery pace while avoiding unnecessary rework.