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*Join Legl as Data Business Engineer
Legl is building the operating system for modern legal services. We help law firms and regulated businesses replace manual, fragmented workflows with intelligent software from client onboarding and compliance to payments, risk, and reporting.
Legal work is high-stakes: regulated, complex, and deeply human. Traditionally, the software supporting it has been slow, manual, and brittle. But it doesn’t have to be that way.
We’re backed by leading European VCs, scaling rapidly, partnered with 550+ law firms (including 40 of the UK’s top 200 firms), and expanding from the UK and Australia into our next phase of growth.
AI-Native by Default
At Legl, AI-centricity is expected and embedded in day-to-day work. Everyone leverages AI to drive quality, speed, and impact, while maintaining judgement and precision—because speed without correctness does more harm than good.
If you treat AI as a multiplier for your craft rather than a threat, you’ll thrive here.
What You’ll Do
As a Data Business Engineer at Legl, you’ll:
- Own business problems, not just data requests:
- Reframing asks, defining real problems, and leading high-impact initiatives—not just responding to ticket-driven demands.
- Driving data-inspired projects that deliver actionable insights over deliverables.
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Own correctness and validation:
- Ensuring data models, knowledge layers, and AI outputs are pre-verified, with wrong answers caught before escalation.
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Maintain the semantic/knowledge layer as a single source of truth:
- Defining metrics once so they’re consistent across all use cases.
- Keeping the data-as-a-product layer accurate and current—viewing it as an ongoing priority, not a one-off task.
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Deliver data-led solutions end-to-end:
- Transforming targets from abstract goals into operational, adoptable outputs (metrics, models, dashboards, automations).
- Driving ownership through adoption and impact, not just delivery.
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Translate between tech and business:
- Bridging the gap for technical and commercial stakeholders by quantifying builds and outcomes in a compelling, actionable way.
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Leverage AI with precision:
- Using LLMs to accelerate work, but judging when agreed outputs are flawed and requiring putback.
- Ensuring robust permissioning and governance around AI-generated insights.
This Role Is a Great Fit If…
✅ Problems first – Your instinct is to ask: "What’s this for?" before building. You evaluate success on impact, not output.
✅ Own ambiguity – You refine vague, high-stakes questions, lead discovery, set clear directions, and ship decisions without perfect inputs.
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✅ Deep data-engineering craft – You’ve solved the hardest parts:
- Protocols for data modelling (standardised joins, slowly changing dimensions, cost models).
- Version-controlled transformations on a cloud data warehouse (e.G. Snowflake, BigQuery, Redshift).
- Treating data as a product, not a query bank.
✅ Single source of truth – You’ve navigated messy historic systems where the same metric was calculated differently—and fixed it.
✅ AI literate – Shipped LLM-driven output before, comfortable with:
- SQL and data structure review to validate AI answers.
- LLM integration into daily workflow.
- Automatic validation where possible.
This Role Is Not a Great Fit If…
❌ Following specs: You treat your role as fulfilling aggregate requests rather than uncovering challenges and boost clarity.
❌ Output over outcomes: Your success measure is junk dashboard volume instead of actual proven game-changing impact.
❌ AI as a black box: You prefer shippable but unverified AI outputs over rigorous technical review.
❌ Mystic or fearful of AI: Never shipped a solution augmented by LLMs before, still isn’t in your day-to-day.
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Jessica, London
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