Head of Data

ABOUT LAWHIVE Our mission is to make the law accessible to everyone. The legal industry is built on technology and processes that haven’t been updated in hundreds of years - that's why we've reinvented the entire model from the ground up with our own bespoke AI operating system at the core. Lawhive is a regulated law firm with an AI-native platform built to amplify expertise and revolutionise the way people practice law, leading to exceptional outcomes for clients and lawyers. Lawhive Labs https://labs.lawhive.co.uk/ is how we bring this vision to life. It's our frontier lab that combines top engineering, design, AI and legal talent from around the world, joining forces to build the future of law. We’re backed by top-tier investors, including Google Ventures, Balderton Capital and TQ Ventures, and in December 2025, we secured $60M Series B funding round to facilitate international expansion and to grow our team. We’re headquartered in London and in 2025 successfully launched in the US…and we’re just getting started. DATA AT LAWHIVE We are building the world's first AI-native consumer law firm, and the data foundations underneath it have to match that ambition. Over the next 12 months, the data function will: - Build out our data stack to enable ingestion, analysis and processing of a growing corpus of data, both to support BI and AI use-cases - Drive data as a product with AI-native consumption so that anyone in the group can explore, dig into, and act on data without filing a ticket - Build the integration playbook for law firms as we expand our firm portfolio. We need a canonical Lawhive data model that scales to all law firms and types of legal work Our current stack: BigQuery, dbt, Hex, Dagster, K8s, Elementary, Claude Code, GCP and AWS infrastructure. We’ll look for strong opinions on best practices and technologies as we scale. THE ROLE As Head of Data, you'll lead Lawhive's data function through a step-change in capability. Reporting directly to our CTO, Jaime https://www.linkedin.com/in/jaime-van-oers/, you'll own the data stack, build the data integration capability for acquired firms, and run a small high-quality team that partners with every function in the business. This role is for someone who has done it before. You've migrated a scaleup's data stack end-to-end. You've integrated acquired-company data into a canonical model. You're AI-native in how you work and in what you build. And you're comfortable telling a CTO "this stack is wrong, and here's what we do instead." WHAT YOU'LL DO - Design and own the acquisition data integration playbook. Build a canonical Lawhive data model that future systems map to. Make firm onboarding a repeatable weeks-not-months process - Partner with Strategy on metric definition. You own instrumentation, semantic layer, and accuracy. They own the metric tree. Together, you run the metric council - Diagnose and improve our data stack in your first 12 months. Propose a target architecture against our goals of AI-native self-serve-first analytics (warehouse, modelling, semantic layer, BI, exploration). Replace tools where needed - Make Lawhive self-serve on data. Build the platform, modelling, and semantic layer that lets business users explore, drill in, and answer their own questions - Define and enforce data quality SLAs. Freshness, accuracy, ownership coverage, end-to-end lineage - Lead and grow the data team. Coach analysts into stronger cross-functional partners. Hire and onboard a data integration engineer in year 1 - Drive AI-native practices inside the data function. Use LLMs for entity resolution, schema mapping, data quality, and exploration. Set the bar for how a data team works in 2026 - Be a key cross-functional partner to Product, Engineering, Finance, Strategy, and the operational teams within acquired firms WHAT YOU'LL BRING You'll be a great fit for this role if: - You're commercially literate. You can partner with functional heads as a peer and translate business questions into data infrastructure - You've owned a data stack end-to-end at a B2B SaaS scaleup. You can walk us through what you built, changed, why, and what you'd do differently - You've built repeatable data integration patterns at an M&A-heavy company or rollup. Ideally, a B2B SaaS context. You know how to handle messy legacy systems and conflicting schemas - You have strong opinions on the modern data stack and data modelling best practices: warehouse, modelling, semantic layer, BI tooling, exploration. You can defend trade-offs - You're AI-native in your craft. You use Cursor, Claude, dbt AI, agentic notebooks daily. You've shipped AI features inside a data team (LLMs for entity resolution, schema mapping, data quality, exploration). You think LLMs change how data work gets done structurally, not just incrementally Nice-to-haves: - Experience standing up a semantic layer (LookML, Cube, dbt semantic laye