Principal Data Engineer

Morela is proud to be supporting a private, entrepreneurial family-owned real estate group with a multi-billion-pound property portfolio and a private credit book. Long established, cash generative, and quietly one of the larger privately held property businesses in the country. Not a name you will see on job boards, because they have never needed to advertise. And, remarkably, no data platform. Property management, asset management and accounting all sit in separate systems that do not talk to each other, which means nobody in the business has a single, trusted view of what it owns.

They could have bought something off the shelf. They could have handed it to a consultancy. Instead, they have decided to build it properly, in-house, and this is the person who builds it. This is a blank page. You choose the stack. You design the model. You decide what gets built and what gets bought. There is no legacy platform to inherit and no architect sitting above you telling you how it is done. Because the business is family-owned, it actually moves: no steering committee, no six-week approval cycle, no business case to defend three times before you write a line of code. You will sit alongside the principals and the operating teams, so what you build goes live and gets used that week. Get the foundation right and the question that follows is a genuinely interesting one: whether the tooling you have built internally becomes a product in its own right.

YOUR ROLE

• Set the direction. Identify and translate the organisation's highest-value problems into a technical roadmap, balancing a robust platform architecture against rapid delivery.
• Build the foundation. Design the data model that operates as a single source of truth, and the pipelines that store and reconcile properties, entities, leases, tenants, projects, invoices, cash flows, valuations and documents across multiple sources.
• Connect the estate. Build integrations with third-party vendors across APIs, webhooks, direct database access and scheduled ingestion.
• Kill the manual work. Eliminate repetitive manual work across internal workflows: data collection, validation, approvals, reconciliations, reminders, document generation and reporting.
• Ship what people use. Deliver dashboards, reports, alerts and internal applications that principals and operating teams can trust and actually use.
• Put AI to work properly. Build LLM pipelines for extraction, classification, retrieval and workflow support, with evaluations, human-in-the-loop oversight and auditability, so it is explainable rather than a black box.
• Set the bar. Set the standards for security, testing, deployment, monitoring and documentation, manage selected vendors, build the engineering culture, and mentor the junior data engineer.

WHAT WE ARE LOOKING FOR

• A strong record of building and operating reliable pipelines, integrations, data models or platforms that serve real users and business-critical decisions.
• Strong programming fundamentals in Python, TypeScript and SQL at expert working level, covering testing, performance and maintainability, with the judgement to review others' code and hold a high-quality bar.
• Confidence building robust REST APIs, working with relational databases such as PostgreSQL, modelling data, running ETL/ELT pipelines, and making pragmatic build-versus-buy calls.
• Experience deploying production systems in AWS, Azure or GCP, accounting for monitoring, secrets management, access control, backups and disaster recovery.
• Experience with production LLM pipelines: context engineering, evaluation, retrieval, tool use and guardrails.
• Experience taking a platform from prototype to production or working in an early-stage environment with a rapidly evolving product.
• Mature judgement handling financially sensitive, personal and commercially confidential information in a regulated industry.
• The ability to set direction, mentor a junior colleague and introduce engineering discipline while staying practical, fast and close to end users.
• Curiosity about how internal tools can be abstracted and generalised into a product.

HELPFUL, BUT NOT ESSENTIAL

• Proptech, fintech, accounting technology, asset management, construction, private investment operations or another data-intensive regulated environment.
• Modern data warehouses, transformation and orchestration tooling, business intelligence platforms and internal web applications.
• Document processing, OCR, search and retrieval systems, workflow engines or human-in-the-loop review tools.
• UK data protection, information security and operational resilience principles.

WHY THIS ONE IS WORTH A CONVERSATION

• Scope most senior engineers never get. The platform is yours to define, not someone else's to maintain.
• Direct access to the people who decide, without the layers that slow good engineering down.
• Real AI work with real constraints, where auditability and evaluation matter as much as the model.
• The stability of a long-established, cash-generative business, with the pace and the blank page of an early-stage one.

THE PROCESS

Morela is supporting this search and managing the process end to end. If the brief resonates, get in touch for a confidential conversation before you commit to anything; the client is named only once you are happy to be represented. Appointment will be subject to appropriate references, background checks and a confidentiality agreement. We welcome applicants from different backgrounds whose skills and evidence meet the requirements, and reasonable adjustments are available throughout the process.