Hybrid Data Engineer: Legal Data, Retrieval & Automation

Hub; København; Hybrid; DKK 391,000 - 502,000; Full time

Job summary

Hub is building a RegTech SaaS platform in Copenhagen to help financial teams manage regulations with data-driven tooling. You will design and implement ingestion pipelines, data parsing, and retrieval systems to support RAG and automated workflows.

The role emphasizes experimentation, data quality, and close collaboration with founders to shape data architecture and product from the ground up.

Qualifications

• Degree in engineering, physics, mathematics, computer science or another quantitative field.
• 2-5 years writing production Python, or PHP with shipped code.
• Strong SQL and relational data modeling experience.
• Scientific approach to data: hypothesis, measure, test counter-examples.
• Experience parsing semi-structured data (HTML/XML/JSON).
• Ability to review AI-generated code critically and safely.
• Careful with production systems.
• Fluent written and spoken English.

Responsibilities

• Build ingestion pipelines querying and scraping regulatory data endpoints.
• Parse HTML/XML legal texts into structured representations.
• Develop LLM pipelines for structured extraction (e.g., which articles change what).
• Create robust, repeatable pipelines that fail loudly without corruption.
• Handle real-world data quirks: bot protection, timeouts, translations, PDFs.
• Expand in-house tools to analyze ingested data and database contents.
• Develop retrieval, RAG and automation to answer regulatory questions.
• Assess compliance and automation impact for client systems.

Skills

Python production code

SQL/relational modeling

Structured data parsing

Analytical mindset

AI code review

Production systems

Fluent English

Education

Engineering/Physics/Math/CS degree

Tools

PHP

TypeScript

BM25/IR basics

LLM extraction tools

Linked-data (RDF/SPARQL)

Job description

Hub is building a RegTech SaaS platform in Copenhagen to help financial teams manage regulations with data-driven tooling. You will design and implement ingestion pipelines, data parsing, and retrieval systems to support RAG and automated workflows.

The role emphasizes experimentation, data quality, and close collaboration with founders to shape data architecture and product from the ground up.