Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Engineer | Systematic Trading

NJF Global Holdings Ltd
City of London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Pricing Data Scientist

Pricing Data Scientist

Lead Pricing Data Scientist

Senior Azure Data Engineer | Pipelines, BI & Mentoring

Data Engineer

Senior Data Engineer - Azure | Permanent | Hybrid

Senior Data Engineer | Systematic Trading


A Top-tier quant fund is hiring a Senior Data Engineer to help architect and scale a global Data Lakehouse that feeds directly into live trading strategies. If you’ve already been building data infrastructure inside a hedge fund or HFT shop, you’ll know what this role really means: low-latency, high-availability, globally distributed data pipelines that quant teams trust every day.

This is your chance to level up from patching legacy pipelines to building the core platform used by quant researchers and traders across desks and regions.


What You’ll Be Driving

  • Architect and build a global Lakehouse platform (Data Lake + OLAP) in AWS
  • Develop scalable data pipelines in Python, working with SDKs and data libs
  • Own end-to-end data modeling, ingestion, validation, and optimization for high-concurrency access
  • Tune performance of cross-region, multi-format data stores (columnar, real-time, etc.)
  • Deliver tailored solutions directly to quants and traders – real impact, real visibility
  • Take the lead on cloud infra design, deployment, and automation

What You Bring

  • 5+ years building data platforms in systematic trading, fintech, or high-throughput environments
  • Mastery of Python and SQL – C++ is a plus
  • Experience with cloud-native stacks (ideally AWS) and interest in infrastructure as code
  • A structured, pragmatic mindset with strong ownership and autonomy
  • Ability to work closely with front-office teams and iterate fast
  • Finance background is a strong plus — you get why PnL depends on good data

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.