What You’ll Do

In your first year, you’ll own the evolution of Lawhive’s data function by:

• Redesigning and driving adoption of the data stack, migrating warehouse (datastores), modelling, semantic layer, BI, and exploration architectures to be AI-native and self-serve-first.
  • Identify tools that must be retired, replaced, or reinvented.
• Own ownership of acquired-firm onboarding: design a canonical Lawhive data model to align with each firm’s legacy systems (usually multiple proprietary systems) and accelerate integration from weeks to days.
• Empower business users to top-line data needs without having to file requests - architect semantic layers, a modelling engine, and exploration platform that transforms analysis workflows for non-technical teams.
• Define and enforce data quality SLAs: freshness targets, accuracy standards, comprehensive lineage.
• Build and support a high-performing data team: develop data analysts into domain experts who forge strong cross-functional relationships, hire and onboard a data integration engineer in year one.
• Partner with Strategy to define, instrument, and govern metrics – while moving key insights from the data stack up to the semantic layer for business users:
  • Strategy owns the metric tree.
  • You own instrumentation and semantic layering for accuracy.
• Roll out AI-native data practices: use an agentic framework to revolutionise daily work: LLM-based entity resolution, schema mapping, data quality assessments, drilldown explorations.
• Join cross-functional forums with Product, Finance, Engineering, Legal, and Management Teams across acquired firms, ensuring seamless data alignment across divisions.

What You’ll Bring

This is the role you’ve been waiting for: it requires owners who’ve moved the needle in data teams and architectures.

Apply if you:

• Have built and scaled an entire data stack for a B2B SaaS scaleup. You’ll be asked to map out your data foundation, justifying reasons for every choice – from architecture, to orchestration tools, to partnerships with AI vendors.
• Have designed repeatable, proven data integration frameworks for asset-heavy rollups or acquisitions. Can demonstrate how you’ve remap incomparable systems into a single coherent model – ideally in complex SaaS landscapes with tightly integrated third parties.
• Create a data product culture, not just Objective-driven or ropey-dashboards: you translate BI to business; or four big SQL queries at 5am into a strategically relevant output.
• Have strong engineering, strategic, cultural partners in data as a vertical product. You don’t tolerate tunnelling into transforms – you believe your capabilities ring into core SaaS utility marketing, engineering, compliance, and beyond.
• Are AI knowledgeable but not overwhelmed: conversant in Cursor, dbt Airflow, horizontal scaling obstetrics, and agentic prompt walls - you not only use LLMs but have rewritten your data stack built by hand, your modelling pipeline, and your documentation through them.

Think Metrics Driven, Domain Agnostic – i.e., layer vertical principles with horizontal instrumental rigour. The metrics you track, how you relate them to your data flows, how decisions are made.

• You’re aware of the operational Izmor index and its relationship to daily averaged events, users, and revenue.**
• You’ve designed role-based data AIs, from executive dashboards to day-2 analyst prompting.
• You’d have your current stack on a thread talking about your ‘DuckDB decision point’ during acquisition rollup.
• You need to act like a c Völker von Praxis in everything you write. Does this suggest **’upgrade entirely’*?

Nice-to-haves:

• Prior experience building semantic layers (LookML, dbt), or declaring the end of the “I’ll just build a duplication language” phase.
• Businessify and turn battle records at loyal investors – expect your strategy alchemy of data rigor and commercial growth to translate into integrated financials for firms.
• exposure to Private Equity-backed rollups – including mirage mechanic handling, conflicting metadata, and wayward statutory schedules in acquired firms with different metrics “really fundamental.”
• BSOL familiarity: Law Society, LE Lead, Licensing, Legal Services Regulations Account and insc ration.

The Interview Process

• TalentIntro call with our Talent team
• ONE - ONE with the CTO, Jaime
• Practical assessment – diagnose an anonymised Lawhive data stack and propose a rewrite
• ONE - ONE with the Head of Strategy
• Values interview with our Founders

What We Offer You

Benefits:

💰 Meaningful early-stage equity (Buy-down ESOP) at a scaling startup ✈️ 33 days annual leave (25 paid + 8 bank holidays) plus your birthday off 💰 Nest-based pension contribution 💷 20% discount on legal fees through Lawhive 💻 Cutting top-spec Macbook perks ⛳ Team-building & socials with cross-division options

Equity & Compensation:

Comp range (base + variable): £120K – £180 – commensurate to scope/age/experience.

Diversity at Lawhive

Diversity of thought is our key to delivering innovation. We prioritise inclusion across the team and continuously redouble efforts to create a dynamic culture that fosters difference and challenges the status quo.

We’re building a high-growth, AI-native future for law. You own every piece. Let’s build this together – Apply now.