MAC (Moves-Adds-Changes) Engineer | Quantitative Analysis and Trading Leader

Techfellow Limited
London
6 months ago
Create job alert
MAC (Moves-Adds-Changes) Engineer | Quantitative Analysis and Trading Leader

[Up to c. £140k Comp Package | On-Site Working]


Role Overview


We’re working with a global trading and investment firm known for its deeply technical environment, where the performance of every workstation and cable connection counts. As part of their continued growth, they’re hiring a Moves-Adds-Changes (MAC) Engineer to support desktop infrastructure in one of the most demanding environments in the industry. This hands-on role runs on the evening shift (3pm–12am) and sits within a broader IT Operations team - covering everything from desktop builds and asset management to user onboarding and relocation work. With limited weekend hours and plenty of room for progression across end-user and systems support, this is a great opportunity to deepen your infrastructure skillset in a high-stakes, low-latency environment.


Key Responsibilities



  • Carry out daily Moves, Adds, and Changes (MAC) activities across desktop hardware and trading floor infrastructure
  • Lead end-to-end workstation setups, including PC imaging, hardware configuration, software provisioning, and user testing
  • Support the relocation, decommissioning, and refresh of desktop technology in a fast-moving environment with no room for downtime
  • Maintain and track IT assets using internal tooling - ensuring accuracy across procurement, stock levels, and deployment lifecycles
  • Contribute to the onboarding of new users - delivering workstation setups, peripheral installs, and environment-specific configuration
  • Assist in infrastructure extension projects, including structured cabling, workstation builds, and peripheral rollouts
  • Collaborate closely with engineering and end-user teams to coordinate delivery of change activities without disrupting trading workflows
  • Help test and roll out new desktop tools and configurations, feeding back on usability and deployment performance
  • Ensure all changes are delivered with minimal disruption, using scripts and automation where appropriate
  • Occasionally assist with physical tasks such as lifting, transporting, or installing equipment

What You’ll Bring..



  • 2-4 years’ experience in a desktop support, infrastructure delivery, or IT operations role
  • Proven ability to manage PC hardware, cabling, peripherals, and enterprise workstation rollouts
  • Experience with asset tracking, user provisioning, and hands-on technology relocation across enterprise environments
  • Confident working evening shifts (3pm-12am) with flexibility for occasional weekend work
  • Highly organised with strong communication skills and the ability to coordinate change activities across teams
  • Technically curious, with a genuine interest in infrastructure performance and operational reliability

Seniority level



  • Associate

Employment type



  • Full-time

Job function



  • Engineering, Information Technology, and Finance

Industries



  • Capital Markets, Financial Services, and Investment Management


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Analyst Placement Programme

HR Data Analyst

Online Data Analyst Training Programme (Sutton)

Scientific Data Engineer - EMEA

Information Architecture Lead - Data Governance & Solutions

HR Data Analyst

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.