Lead Data Engineer

Legal 500
London
1 month ago
Applications closed

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About Legal 500

Legal 500 was founded by John Pritchard in 1987 as the original clients’ guide to law firms, the first of its kind. It is now a data-driven, AI-optimised research platform which benchmarks, informs and connects providers and users of legal services in over 100 countries worldwide.


Our research and data are trusted and relied upon by corporate clients globally as an essential part of the process, both of instructing law firms with new mandates, and when reviewing existing mandates or panels.


We exist to empower both buyers and sellers in the international legal marketplace to make better decisions and have improved outcomes for their organisations. This is achieved by leveraging a trusted, comprehensive research process with a unique, vast, proprietary and constantly updated set of client-supplied data, unrivalled in the market.


On the supply side of the legal market, every year Legal 500’s team of over 150 researchers, technologists, data analysts, journalists and content specialists collate and review 60,000+ data-submissions from law firms and conduct interviews with thousands of leading law firm partners. On the demand side, Legal 500 analyses confidential data from 300,000+ commercial law firm clients to benchmark law firms and lawyers by practice area; industry; jurisdiction; as well as by proprietary client satisfaction metrics, NPS®, and other qualitative and quantitative criteria.


Legal 500 is the only source of this depth of global research and data on law firms, lawyers and their clients.


The Role

As our Lead Data Engineer, you’ll be the senior technical voice in the data team and a critical partner to the Head of Data. You’ll design, build, and improve our Snowflake and dbt-driven platform, establish engineering best practices, and support the growth of our data capabilities as the organisation scales.


You’ll work in a Microsoft-first environment, using Azure cloud services to orchestrate, automate, and deliver robust, production-ready data pipelines.


This is a hands-on leadership role with real ownership and the opportunity to bring modern engineering discipline into a growing function.


What You’ll Be Doing

Platform Ownership & Architecture

  • Lead the architectural direction of our Snowflake-based data platform
  • Design scalable ELT pipelines and transformation layers using dbt
  • Build high-quality data models across staging, intermediate, and marts layers
  • Make architectural decisions around modelling approaches and data lifecycle design


Data Engineering & Delivery

  • Develop and optimise transformations in dbt and SQL
  • Use Azure services (e.g., Azure Data Factory, Azure Functions, Azure Storage) to orchestrate and deliver pipelines
  • Introduce CI/CD, testing, code quality, observability, and documentation best practices
  • Improve performance, cost efficiency, and reliability of the platform


Leadership & Continuous Improvement

  • Set engineering standards, patterns, and technical guidelines for the team
  • Mentor and guide engineers and analysts
  • Partner closely with product, software engineering, and research teams
  • Drive a culture of ownership, collaboration, and delivery excellence


What We’re Looking For

Technical Skills

  • Strong experience with Snowflake (performance tuning, warehouses, modelling, optimisation)
  • Deep experience with dbt (tests, macros, documentation, project structure)
  • Excellent SQL skills and strong data modelling foundations
  • Experience designing and building ELT pipelines
  • Experience using Azure cloud services for data workflows (e.g., Data Factory, Azure Functions, ADLS)
  • Solid understanding of version control, CI/CD, testing, and engineering best practice


Leadership & Ownership

  • Experience leading or steering data engineering initiatives
  • Ability to introduce structure, standards, and long-term thinking
  • Comfortable influencing cross-functional teams and mentoring others
  • Pragmatic, delivery-focused approach with strong communication skills


Interview Process

  • Screening Call with Talent Partner
  • 1st Stage with the Head Of Data
  • Technical Test – Take Home
  • Final Interview

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