Staff Data Engineer

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About the role

We're seeking a Staff Data Engineer to join one of the fastest-growing AI legal tech companies in Europe. You'll take technical ownership of the data platform behind how we run the business: the pipelines, the warehouse, and the definitions behind the numbers our leadership team actually uses. You'll report to our Head of Engineering and work alongside a colleague who owns the business-facing side of data, plus our infrastructure and application engineers. This is a senior individual contributor role with no line management in either direction, roughly 70% hands-on building, 30% defining, documenting and explaining. Your Challenge and Opportunity in This Role JUPUS supports hundreds of law firms across Europe, and our customer base is growing roughly 4x a year. Our data platform has grown with it (ClickHouse, dbt, Python extract-load pipelines into an S3 data lake, orchestration in GitHub Actions) and it has grown fast. Your job is to make it trustworthy. That means modernising what already exists, making historical trends a record of what actually happened rather than a nightly re-derivation of it, and owning the definitions behind the metrics that reach our board. This is not a greenfield build, and it is not a maintenance seat. It is a real platform with real problems, and you would own it. Tasks • Own the data platform end to end: ingestion and the raw layer, the ClickHouse warehouse, and the dbt project on top of it. You're the person who can say whether a number is wrong or the pipeline is. • Make our numbers trustworthy: reliability, correctness, and a history that holds up, so that a trend is a record of what actually happened rather than a re-derivation of it. • Own the metrics the company runs on , starting with our customer health score- the definition, the denominator, the eligibility rules and the known limitations, not just the SQL underneath them. • Turn business questions into durable metrics: work directly with Customer Success, Sales, Product and leadership to understand what they're actually trying to decide, then build something defensible enough to decide on. • Keep cost and complexity in check as we grow: warehouse architecture, query cost, and the surfaces that deliver data into BI and our CRM. All of it gets harder as the customer base multiplies. • Set the standard for how data work is done here: one authoritative written definition per leadership-visible number, and the conventions that inform our Data function as it grows with us. Requirements Technical Expertise • Substantial experience in data engineering or analytics engineering , with production ownership of a warehouse and a transformation layer. We care more about evidence of ownership than about years served. • ClickHouse in production. • dbt at ownership level: incremental models, snapshots, tests, macros, CI/CD. Ideally you've inherited someone else's dbt project and left it better than you found it. • Python data engineering: EL tooling, S3, CI-based orchestration that you’ve owned end to end, with no platform team behind you. • Product event streams: PostHog, Amplitude, Segment, Mixpanel or similar. You understand how user-to-company identity and group association break, and what that does to every metric downstream. Judgement and Ownership • You have defined a business KPI end to end and defended it to non-technical people. This is the differentiator for us. An excellent engineer who takes requirements as given is not the right fit for this role. • Analytical rigour: cohorts, correct denominators, sample size stated before findings. You'll say "we can't tell yet, and here's exactly why" rather than produce a confident chart. • You act without waiting for a ticket , and you can name something you chose to stop doing. • You'r

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