Senior Data Engineering Manager

Xcede
London
4 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineering Manager

Senior Data Engineering Manager

Data Engineering Manager

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Hybrid/Remote

Overview

We\'re partnered with a leading European digital platform who are at the cutting edge of data-driven product innovation. With an ambitious, product-led strategy and large-scale investments in technology, they\'re building some of the most advanced real-time data platforms in the industry. As part of a global group with a strong footprint in regulated markets, they combine agility with deep technical maturity, offering engineers a highly stimulating, impactful environment with real ownership, autonomy, and the chance to influence system design at scale.

We are looking for a Senior Engineering Manager to lead and shape a cross-functional team delivering high-volume data products that power dynamic pricing and sports modelling capabilities across a large-scale platform. These products feed live environments where performance, accuracy and speed are business-critical.

About the Role

This is a \'management first\' but highly technical leadership role perfect for a senior engineering leader with a background in real-time, event-driven architecture and a passion for building high-performing teams. We\'re open to a Principal IC who has mentored others if they have headed up real-time dynamic pricing engineering builds.

You\'ll lead a cross-functional team responsible for delivering complex, high-volume data products that power dynamic pricing and sports modelling capabilities. You\'ll manage both people and programs, guiding quarterly planning cycles, unblocking technical delivery, owning roadmaps, and supporting professional growth within the team. With direct responsibility for ~5–10 engineers (including other managers or staff-level ICs), you\'ll be central to growing and shaping the future of this function.

Responsibilities
  • Leading and scaling a team of engineers focused on delivering high-performance quantitative models into production environments.
  • Defining strategic goals and setting quarterly engineering milestones in partnership with product and senior stakeholders.
  • Owning cross-functional planning and delivery for engineering workstreams across multiple squads.
  • Overseeing the build and evolution of scalable frameworks, real-time systems, and data assets to support quant model deployment.
  • Driving the team\'s contribution to overall technical architecture, tooling, and infrastructure.
  • Managing technical risks, resolving blockers, and ensuring team alignment with wider engineering functions.
  • Supporting hiring, succession planning, and team culture initiatives.
  • Acting as a visible ambassador for engineering excellence, both internally and externally.
Requirements
  • Proven leadership experience managing engineering teams, ideally within quantitative, pricing, or data science domain. 5+ direct reports preferred.
  • Experience overseeing delivery of large-scale, data-driven products from prototype through to production in complex environments.
  • Strong technical understanding of real-time event-driven data processing systems including Kafka or similar frameworks.
  • Hands-on knowledge (past or present) of Java and AWS-based cloud environments, with an understanding of scalable system design.
  • Exceptional stakeholder management and communication skills; able to engage across senior business, product, and engineering stakeholders.
  • Experience in setting engineering strategy, team direction, and owning long-term goals and outcomes.
  • Comfortable navigating between high-level strategic leadership and deeper technical conversations.
  • Familiarity with Agile delivery, release planning, and quarterly OKRs.
Nice to Have
  • Prior experience leading managers or principal/staff-level engineers.
  • Background working in environments focused on live pricing, sports modelling, or financial engineering.
  • Postgraduate degree (MSc or PhD) in a STEM field.
Employment details
  • Seniority level: Not Applicable
Employment type
  • Full-time
Job function
  • Information Technology
Industries
  • Software Development

If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via (feel free to include a CV for review).


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.