UK restructuring and insolvency advisory with data analytics backbone
Leonard Curtis is a 300+ person restructuring and insolvency firm building a modern data platform. Their tech stack reveals a significant engineering effort: Databricks + Microsoft Fabric + Azure DevOps, with active projects spanning lakehouse architecture, real-time reporting pipelines, and Delta Live Tables automation. The hiring mix (data roles dominating, paired with legal and finance) signals a shift from advisory-only toward operationalized data-driven service delivery—a natural move for a firm handling financial distress, debt, and VAT recovery cases where data pipeline reliability directly impacts client outcomes.
Leonard Curtis advises business owners, lenders, and their advisers on restructuring, insolvency, funding, M&A, and related legal services across the UK and offshore. The firm operates five service pillars: rescue and recovery, debt advisory, HMRC liaison, property solutions, and refinancing guidance. As a Nexia member, they integrate with a broader international network. Their recent pivot toward data infrastructure—removing silos, automating reporting, and centralizing financial assets—reflects growing reliance on analytics to support due diligence, cashflow forecasting, and turnaround planning.
Databricks (SQL, PySpark, Unity Catalog, Delta Live Tables), Microsoft Fabric (OneLake, Data Factory, Notebooks, Direct Lake), plus Power BI for real-time reporting and Azure DevOps for orchestration.
Designing a medallion lakehouse combining Databricks and Fabric, building real-time Power BI reporting, automating Delta Live Tables pipelines, and establishing data factory workflows to centralize financial assets and remove data silos.
Leonard Curtis's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.