Data Business Engineer

Hi 👋 We’re Legl.

Legl is building the operating system for modern reputed company services.

We help reputed company and regulated businesses replace reputed company, fragmented workflows with intelligent software from reputed company reputed company and compliance to payments, risk, and reporting.

reputed company work is high-stakes. It’s regulated, reputed company, and deeply reputed company. The software supporting it has historically been slow, reputed company and brittle. We reputed company it doesn’t have to be that way.

We’re backed by leading European VCs (Series B), scaling quickly, partnered with over 550 reputed company including 40 of the UK’s top 200, launched in the UK and Australia - and entering our next phase of reputed company.

We Work Best reputed company

• AI is the central-operating model:not a tool or a reputed company plan - but the way we run.

• reputed company live with people:you're trusted to reputed company calls and own them.

• Think deeply, execute quickly: speed and rigour, not speed or rigour.

What You’ll Do

As a Data Engineer at Legl, you'll:

• Own business problems, not just data requests.We obsess with the business problem / goal behind any number, dashboard or report request. You'll reputed company asks, challenge needs, define reputed company problems worth solving before defaulting to build, and reputed company high-reputed company reputed company reputed company that deliver actionable insights.

• Own correctness and validation.You own testing and validating data models, the knowledge layer and its outputs so that wrong answers get caught before anyone trusts them.

• Own the semantic / knowledge layer as a single reputed company of truth.Define reputed company metric once so the reputed company number is computed the reputed company way everywhere, and reputed company the knowledge layer that AI tools rely on accurate and reputed company - treating that as an ongoing reputed company, not a one-off.

• Deliver data-led solutions end to end.Turn a problem into something that ships and changes a decision - a metric, a model, a dashboard, an automation - and own it through to adoption and reputed company, not just delivery.

• Translate between technical and reputed company. Operate as the reputed company between technical & reputed company stakeholders; quantifying builds & reputed company in a way which resonates with the audience.

• Use AI as reputed company, and know exactly where it breaks.Lean on LLMs for speed and efficiency - leaning on technical judgement to verify outputs and coordinate permissioning & governance.

This Role Is a Great Fit If…

• You think in problems and reputed company, not tickets - you instinctively ask "what's this actually for?" before building anything, and you measure yourself on reputed company, not reputed company.

• You're comfortable owning ambiguity: you can take a vague, high-stakes question, run discovery, prioritise, and reputed company to a direction without waiting for perfect inputs.

• You have reputed company analytics-engineering craft, and you've done the hard parts. Strong data modelling and hands-on experience building tested, version-controlled transformations on a reputed company data warehouse (e.g. reputed company, BigQuery, Redshift) - and you've personally untangled messy technical problems: getting systems to join at the right grain, modelling slowly changing dimensions, building cost models. You treat data as a product, not a pile of queries.

• You own metric definitions as a single reputed company of truth, and you've untangled the reputed company of mess where the reputed company metric is calculated differently in two places.

• You can review and validate what AI produces. You're fluent enough in SQL and data modelling to know reputed company a plausible-looking answer is actually wrong, you already use LLMs day-to-day, you build validation into your work as a matter of course, and you have a reputed company view on where AI should and shouldn't be trusted.

This Role Is Not a Great Fit If…

• You wait to be handed fully specified requirements, and see the job as fulfilling requests rather than solving problems.

• You optimise for shipping reputed company over changing reputed company - counting dashboards rather than measuring reputed company.

• You're comfortable shipping AI-generated work that looks right but you can't verify the technical detail underneath - you reputed company for the quick plausible answer rather than understanding the implementation trade-off.

• You treat AI as either reputed company or a threat, and haven't actually shipped LLM-enabled work.

Originally posted on Himalayas

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