Senior KDB+ Specialist – Fixed Income Algo Trading & Analytics

Descripción del puesto

Join MARGO in the UK (London) – Perm & Contracting Opportunities MARGO is a tech-native consultancy delivering high value-added IT expertise to the financial services industry. We specialise in data-intensive trading and risk environments where real-time analytics, large data volumes, and high-performance engineering are critical . Joining MARGO means working on highly specialized data platforms while benefiting from a human-sized structure, close follow-up, and tailored career paths . Business Context You will work directly within the Front Office Fixed Income Algo Trading IT environment . This role sits in a fast-paced setting focused on real-time pricing, risk management, and trading algorithm performance for FI products . The platform supports: Real-time and intraday market data streaming and time-series capture . Fixed Income pricing, hedging, and risk analytics . Ad-hoc statistical analysis of algorithm behavior, client behavior, and market impact . Real-time trade and algorithm monitoring dashboards (e.g., Panopticon) . Role Overview We are looking for a Senior KDB+ / q Specialist to work on real-time data and algo trading platforms . While centered on robust kdb+ development, the position features short feedback cycles and direct interaction with traders and production teams . We welcome candidates with strong kdb+ / q experience who thrive across: Core kdb+ / q development & time-series architecture 3rd line platform support & performance optimization Technical business analysis & trader-facing requirement gathering Key Responsibilities Development & Analytics: Design, develop, test, and deliver change on real-time KDB+ processes, including API and schema design for FI Algo desks . Business Interaction: Liaise directly with traders to gather requirements, analyze algo performance, and build custom monitoring views (such as Altair Panopticon dashboards) . Platform & Data Ownership: Maintain and evolve time-series data models, optimize