Backend Engineering Lead

London
2 weeks ago
Create job alert

My client is looking for a highly skilled and motivated engineer to lead our back-end engineering team. The back-end team is responsible for designing, implementing and maintaining the systems and services powering our rapidly growing, award-winning technology offering.

Key Responsibilities:

  1. Back-end Tech Strategy and Leadership

    Develop and execute the back-end strategy for our financial and quantitative applications, working to break down and complement objectives from wider business and technology team strategies
    Provide leadership to the back-end team, acting as an expert for Rust & Python, fostering a culture of innovation and leading adoption of best-practices2. AWS Cloud Solution and Backend Architect

    Design, architect and implement back-end services as part of cloud-native solutions on AWS, building Python-based APIs and Rust-based event-driven microservices utilising gRPC
    Create suitable design documentation and supply sufficient technical detail to tickets for more junior colleagues3. Team Management

    Manage a team of back-end and financial software engineers, overseeing the team delivery using AGILE processes
    Provide mentoring/coaching to reports, improving team code quality through the pull request process, and conducting bi-annual performance reviews4. Collaboration and Stakeholder Management

    Work closely with business stakeholders, including finance teams, traders, and risk management, to understand their needs and translate them into effective technology solutions.5. Financial Market Knowledge

    Design data models for elegantly capturing the business domain of complex trades across multiple asset classes. Build a strong understanding of FX and interest rate products, the related financial markets, and market data sources

    Requirements:

    Bachelor's degree or higher in computer science, mathematics, finance or a related field.
    Work closely with business stakeholders, including finance teams, traders, and risk management, to understand their needs and translate them into effective technology solutions.
    Proficiency with both Rust and Python. In exceptional circumstances we may consider applicants with a bulk of experience in a low-level language other than Rust (such as C++ or Go), though a minimum of a demonstrable understanding of Rust concepts and best practices is required.
    Experience leading or mentoring a team of engineers.
    Experience developing cloud-based services in a microservices-led architecture.
    Curiosity to explore new technologies. We are constantly looking for potential technologies to improve our platform.
    Strong understanding of financial markets, quantitative modelling and related data models.
    Strategic thinking with the ability to plan and architect solutions in line with technology initiatives and wider business objectives.

    Preferred Qualifications/Experience:

    Experience with financial or market risk modelling is a plus, but not required. The role is heavily finance focused, so a keen interest is expected.
    Knowledge of AWS computing platforms and services

Related Jobs

View all jobs

Data Engineer / Back End Developer - UKIC DV

Backend Engineer - Customer Risk Monitoring (MLOps Growth Path)

Staff Full Stack Engineer

Head Mobile Backend Development (Equity only)

Solution Architect - Identity Management Software platform – remote outside IR35 contract

Solution Architect - Identity Management Software platform – remote outside IR35 contract

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.