Lead Analytics Engineer

London, Waterloo (Hybrid, 4 days in-office, Wednesday is our set work-from-home day, though you can come in on Wednesday too if you wish) We're disrupting one of the world's largest asset classes, property. With £4Bn+ assets on our platform and 30,000+ users across 70 countries, we're building the future of asset ownership and, in doing so, addressing wealth inequality. Our product simplifies property investing from start to finish, making real estate investment accessible to everyone. THE OPPORTUNITY Our ambition is to hold the most complete context of any property investor anywhere (every decision, document, transaction, and signal) so that whatever an investor needs to be successful can happen inside GetGround. Good decisions need complete context. That's the data problem we're solving. We have a data architecture that already collects, joins, curates, and surfaces the data we generate. The next phase is harder. We need data that's clear, unambiguous, trusted, and usable across the business (not just by the technically confident). We need to simplify how we work with it. And because we ship quickly, the data layer has to keep pace with logic and schema changes rather than fall behind them. We're looking for someone curious and outcome-driven to lead this work. You'll inherit the function from our outgoing Lead Analytics Engineer, so you'll need to land, ramp quickly, and start making decisions. You'll be the data function for a while, with the remit to define how it grows. You'll report to our Product Director and partner closely with Product, Engineering, Growth, RevOps, and Finance. WHAT YOU'LL BE DOING - Building trust in our data. Right now, getting the right answer requires knowing the business AND the data. Your job is to close that gap so anyone (including AI-assisted users) can get reliable answers. - Owning the analytics platform end-to-end (from raw sources through to the metrics that power business-facing reporting). - Diagnosing problems wherever they live. Symptoms in one layer don't always have causes in the same layer. You'll need to be able to follow a data quality issue back to its source (and decide where to fix it). - Simplifying, not stacking. We’re fine with fewer tools and clearer data, not more layers on top. - Modelling our core entities properly. Users, ownership structures, properties, transactions (the data model that underpins everything we report on, with the nuance our business actually needs). - Governing the metrics layer. From Finance, to Product, Growth, and Compliance. - Writing clear, maintainable code and thoughtful documentation (for humans and for AI models). Setting the engineering bar for our data culture as the team grows beneath you. - Mentoring and hiring. Within ~12 months we'd expect you to be hiring and developing an Analyst or second Analytics Engineer beneath you, and lifting the data literacy of product and engineering teams across the business. WHAT WE'RE LOOKING FOR - Systems thinking and first-principles reasoning. You map before you act. You're comfortable saying "this is the wrong question" and reframing. You spot root causes other people miss because they're looking at the symptom. - A bias for simplification. You've removed more code and tools than you've added. You can tell the difference between essential and accidental complexity, and you push hard on the second. - Engineering across the stack, not just the warehouse. You can read application code and reason about it. You can navigate a Postgres or MySQL schema and tell what's well-designed from what's accreted over time. You're comfortable opening a pull request against a production codebase to fix a data problem at source, not just patching it downstream. - Strong engineering fundamentals. You write clean, tested, maintainable code; you understand the systems your data flows through; you can read a schema and reason about why it was designed the way it was. - Strong SQL: you can read and write it fluently, includ