Senior Data Platform 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.

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