Senior Engineer, Commodities Technology
London
You will ingest vendor feeds, define schemas and data models, load data into analytical stores, and build controls that identify incomplete or…
Location: LondonDepartment: TechnologyExperience Level: Experience ProfessionalsContact: Paul KrugJob ID: REQ7747Our Forward Deployed Engineers work with the full range of our Portfolio Management groups to maximize the impact and effectiveness of the comprehensive technology platform underpinning our market-leading business. Working directly with our users and partners, we build out tools and APIs and integrate quant models to support risk management, pricing, backtesting and trade lifecycling. This role balances financial markets experience and expertise with high-level understanding of the large Macro tech stack in order to develop the innovative applications, analytics and infrastructure our PMs and analysts use every day.In this role, you will: • Build and refine libraries and APIs to expose ever-expanding quant analytics to platform and user teams• Work on designing and implementing live trading tools, services and applications that help PM pods scale their research and capture alpha.• Develop rapid applications with a strong focus on system design and clean APIs that integrate well with wider firmwide core tech platform.Ideal candidates will be:• Eager to learn and solve a wide variety of systematic problems across a broad range of financial instruments including Rates/FX/Options/Futures.• Entrepreneurial and strategic in identifying opportunities while working with the wide set of PM teams to scale the macro tech platform, suggesting scope to build apps or merge apps as the use cases expandRequirements: • 2+ years professional/commercial development experience – strong preference for Python, and ideally working in a trading floor environment• Familiarity with pricing and risk management of Interest Rate and FX products including swaps, futures and options• Desire to work directly with demanding clients and proactively grow financial domain knowledge• Proven record in fast-paced environment with strong time management and prioritization skills• Background in Data Science, Computer Science, Engineering or STEM
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