UK retirement specialist delivering annuities, equity release, and de-risking solutions
Just is a FTSE-listed financial services group focused on retirement income and longevity risk management. The tech stack reveals a hybrid enterprise architecture — Azure cloud infrastructure paired with Python, SQL, and modern web frameworks (Next.js, React) — suggesting active modernization alongside legacy financial systems (Bloomberg, Refinitiv, SQL Server, Dynamics 365). Active projects around medallion-architecture data transformation and a real-time behavioural insights system signal a push toward data-driven product decisions, while pain points around operational risk, key person dependencies, and stress-testing accuracy point to a company scaling governance and analytics capabilities ahead of regulatory demands.
Notable leadership hires: Head of Engineering, Head of Data, Data Insight Lead, Business Communications Lead
Just Group plc is a UK-listed retirement specialist providing defined benefit de-risking, annuity solutions, equity release products, and care planning services to individuals and institutional clients. The company operates across retirement income, medically underwritten products, and protection, with a stated focus on helping people manage later-life challenges. With 1,001–5,000 employees based in Reigate, Surrey, the organisation is mid-market in headcount but managing complex actuarial and regulatory requirements typical of listed financial services firms. Current hiring emphasis is on finance and engineering roles, with active work on data foundations, internal models, and analytics platforms.
Just runs TypeScript, Next.js, React, and Python on Azure infrastructure, backed by SQL Server, MongoDB, Databricks, and Power BI. Legacy integration includes Bloomberg, Refinitiv, Microsoft Dynamics 365, and Litmus.
Current projects include member option calculation tools, medallion-architecture data transformation, a real-time behavioural insights system, secure data foundation implementation, and modernisation of internal risk and operational models.
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