Job Description
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, including spotting the bits AI tools get wrong.
Python fluency: enough to write a Dagster asset, hack together an integration, or run an ad-hoc analysis without friction.
dbt familiarity. You should be comfortable in dbt projects and understand the underlying patterns (incremental models, tests, documentation, lineage).
Comfortable across multiple databases and warehouses. BigQuery experience is a plus; comfort moving between transactional databases (Postgres, MySQL) and warehouses matters more than fluency in any specific one.
A semantic-layer mindset. You've worked with a metrics catalog or similar, and you understand WHY one matters (not just how to build one).
Business curiosity. You'll be close enough to Finance, Product, and Growth that you'll need to understand what they're asking, not just answer it.
AI-native workflow. We expect you to use Claude, Cursor, or equivalent daily (for SQL, for code, for documentation, for analysis). We'll ask about this at interview, and how you describe your AI use will matter.
Solo or near-solo experience. You've been the data person somewhere before, or close to it. You're comfortable prioritising independently, owning outcomes, and saying no.
A track record of mentoring or supporting other engineers as you've grown senior.
THIS ROLE ISN'T FOR YOU IF
You want fully remote work. We're hybrid, 4 days in-office.
You self-taught SQL on the analyst side and have never worked in application code. The role goes deeper than the warehouse and we need someone comfortable across the stack.
You'd rather add a new tool than understand the underlying system. Our challenge is simplification, not expansion.
Your medium-term ambition is to move into pure product engineering or away from data work. We need someone who wants this role for what it is.
You prefer being handed requirements rather than defining the analytics agenda yourself.
You need a large data team around you to do your best work — for the first 6–12 months you'll be it.
You're skeptical of AI in your workflow. We're an AI-first culture and the role assumes you've embraced it.
You're looking for a slow-paced environment. We move fast, own outcomes, and live by our values: no BS, pursuit of excellence, feedback obsession, and healthy ego.
INTERVIEW PROCESS
Recruiter screen (30 min)
Technical conversation (30 min) Focused on systems thinking, how you've handled messy data problems in the past, and how you approach problem identification. No coding, this is to make sure the role and your background fit before we ask you to invest time in a take-home.
Technical take-home: A combined exercise: a system-design discussion task using a portion of GetGround-shaped data, alongside a short build exercise testing SQL, dbt, and Python. AI use encouraged. ~2–3 hours of effort, completed in your own time over a week.
Technical onsite (1.5 hrs) 15-min demo of your take-home, followed by a live exercise covering SQL, product intuition, and how you'd approach a real GetGround data problem end-to-end.
Final conversation with senior leadership.
GetGround provides visa sponsorship for this role (if necessary)
Benefits:
💰 Competitive salary
💰 Stock options
🏥 Private health & dental
🏖 12 Mental health days off annually (1 per month) PLUS holidays + public holidays
💚 Monthly wellness budget
🫶 1 paid Community day off (paid day off to volunteer for a charitable cause)
🏡 Hybrid working
🥣 Free breakfast daily
🎳 Team and company-wide events
📈 360° performance reviews to promote a culture of growth and development
🎤 Support for conferences and professional learning & development
What we are building: The first end-to-end real estate investment offering - making the dream of owning real estate more accessible to everyone globally.
Diversity & inclusion at GetGround: We encourage applications from all sections of society and we believe in the criticality of an inclusive culture. We are committed to equal employment opportunity regardless of race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity or any other basis as protected by law.
42% of our employees identify as female or non-specified, 58% as male
22 nationalities represented across offices in 5 countries
Our work on Design Accessibility
Inclusion is at the heart of our culture - we celebrate and reflect on key D&I and cultural events such as: Black History Month, International Women's Day and Pride
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