Senior Software Engineer - Fixed Income & Derivatives

Bloomberg
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Software Engineer

Senior Software Engineer and Team Leader

Senior Software Engineer (Data Engineering), WAN Insights. (Basé à London)

Senior Software/Data Engineering Lead- Global Investment Bank | London, UK

Senior Software Engineers

Software Engineer

Senior Software Engineer - Fixed Income & DerivativesLocation: LondonBusiness Area: Engineering and CTORef #: 10041190Description & Requirements Bloomberg is a global leader in business and financial information, delivering trusted data, news, and insights that bring transparency, efficiency, and fairness to capital markets. The Fixed Income and Derivatives Engineering team produces world-class applications and tools that enable our clients to generate trade ideas, structure deals, connect to electronic trading platforms, capture market movements, and assess and hedge portfolio risk for a variety of financial instruments across fixed income and derivatives asset classes.

If you want to know about the requirements for this role, read on for all the relevant information.

While building innovative technology is at the core of what we do, our group also develops sophisticated solutions for ever-evolving financial markets. We work directly with product managers, financial engineers, and quantitative analysts to understand client and market needs. We use cutting-edge big data technologies, distributed computing, functional programming, and machine learning to build software solutions that help us implement complex financial and quantitative models to facilitate derivatives pricing and analytics in real-time.

What’s in it for you?

As a member of the Fixed Income and Derivatives Engineering team, you'll contribute to a high-performance financial software system that handles billions of calculations per day. You'll gain hands-on experience in data analytics, distributed algorithms, and performance-optimized code; all while gaining an advanced knowledge of financial instruments and markets. We seek passionate engineers who thrive in a diverse, collaborative environment and excel at crafting maintainable, efficient solutions to complex problems. Proficiency in object-oriented programming languages like C++, Python, or TypeScript is greatly desired, with a willingness to learn new technologies. You will also have the opportunity to leverage open-source tools like Apache Kafka, Spark, Cassandra, and Redis (plus many more!) to design, develop, and implement full-stack solutions, adhering to industry best practices for software development, testing, automation, and CI/CD.You'll need to have:

Experience working with an object-oriented programming language (C/C++, Python, Java, etc.)A degree in Computer Science, Engineering, Mathematics, a similar field of study, or equivalent work experienceProficiency in system design, architecture, and development of high-quality, modular, stable, and scalable softwarePassion for leading discussions, sharing innovative ideas, and promoting best practices within the teamProficient in adapting project execution to meet evolving demandsWe'd love to see:

An interest in financial markets or a background in data analytics or financial engineeringExperience with high volume, high availability distributed systems

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.