Rainmaker
Senior Data Engineer
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About Rainmaker
Rainmaker is built to give lawyers business intelligence for Business Development (BD): legal news and deal data, a BD tool for building and managing approaches to prospective clients, plus a live rankings system that lets lawyers credential their work against their peers. Product set for launch later this year. This is an AI-first product in the legal tech space; you'll get to work with a disruptive product as it impacts a new market.
Where we are
We are early stage and pre-product-market-fit, launching to first customers in November. The platform was built with an external engineering agency and we are bringing engineering in-house over the coming year. The data platform is the deepest part of what exists: an evidence-based medallion architecture on AWS with master data management at its centre, feeding lawyers, firms, deals, moves, news and rankings into the product. Around fifty law-firm websites have been onboarded so far. You will take ownership of all of it.
The role
You will own Rainmaker's data platform end to end, from raw collection to the facts the product serves, and be the person who understands why any number on the page is what it is. You join alongside the agency's data architect and data team lead, learn the system from them, and take it over as they roll off. You report to our Head of Engineering, working closely with our CTO and Head of Product on what the data needs to say, and with the AI engineer whose enrichment feeds your pipeline.
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What you'll own
- The medallion pipeline. Bronze source evidence, Silver observations, Gold facts with provenance and confidence, and the computed views the product reads.
- Master data management. Entity resolution, which observations describe the same lawyer, firm or office, and reference resolution between them. The crosswalk, the confidence scoring and the audit trail.
- Firm onboarding. Each law firm's site needs its own configuration and breaks in its own way. You own the onboarding process, the data-quality rules that catch failures, and the decision about how our data onboarding scales.
- Data quality. The framework, the rules, and the credibility of published figures: deal values, rankings inputs, dates; where a wrong number costs more than a missing one.
What you'll do
- Learn the architecture from the people who built it, and take over its operation in a defined handover.
- Run and improve firm onboarding: dev, staging, production, with QA sign-off at each step.
- Rebuild rather than patch. When Gold is wrong, fix the transformation and replay from Bronze.
- Define the ingestion path for AI-proposed facts, so model output enters as source evidence and is resolved, never written directly to truth.
- Work with the platform engineer on the AWS estate the pipeline runs on. Step Functions, Spark, RDS, S3, OpenSearch.
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What we're looking for
- You have owned a production data platform and pipelines with real consumers, and understand the difference between data that is complete and data that is right.
- You have done entity resolution or master data work and can talk about matching keys, confidence thresholds and what happens when two sources disagree.
- You are strong in Python and SQL, at home on AWS, and have run Spark or an equivalent at scale.
- You care about provenance: every fact traceable to a source, every change versioned.
- You communicate clearly with non-engineers about what the data can and cannot support.
- You are AI-native in how you build. You use AI coding tools such as Claude Code as a matter of course, to write and review code, to read an unfamiliar system quickly, to test and document, and you know where they are unreliable and check accordingly. We build an AI product with AI, and we expect the team to work that way.
- We do not screen on years of experience or degrees. Show us a platform you owned and a data-quality problem you solved properly.
How we work
Small, direct and close to the founders. You will pair with the agency's data engineers for your first months, with a clear handover you help define. Our office is in London; we are happy to consider remote candidates who can come to London occasionally.
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