getground - Senior Analytics Engineer

• 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. • 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)