Finance System Assurance Manager

Glascoed
9 months ago
Applications closed

Related Jobs

View all jobs

Business Intelligence Development Manager

Data Analyst

Senior Data Scientist

Data Quality Assurance Analyst

Senior Data Scientist

Emerging Markets Quantitative Desk Strategist

Finance Systems Assurance Manager
£40 per hour PAYE
24 Months
37hrs per week
Frimley or Glascoed (Hybrid working – highly flexible)
Inside IR35

We are seeking an experienced Finance Systems Assurance Manager to lead on financial systems assurance, corporate reform compliance, and internal control excellence. This role will sit within the central finance function and act as a key interface across IT, Group Process Ownership (GPO), and external/internal audit stakeholders. The successful candidate will champion systems control best practice and drive a culture of compliance and continuous improvement.

Key Responsibilities:
• Lead assurance across finance systems to support corporate reform and compliance requirements.
• Act as the key interface between Finance, GCC, EIT, GPO, and other internal/external stakeholders.
• Drive high standards of financial systems controls and data governance.
• Create, maintain, and embed a robust controls culture across the wider business.
• Own the delivery of system controls aligned with transformation initiatives (e.g., Ad Astra programme).
• Oversee Internal and External Audit deliverables and support timely resolution of audit actions.
• Provide leadership (without direct line management) across a matrixed systems controls team.
• Line manage direct reports within the systems team including COSA quarterly reviews and MetricStream ownership (NXG & E-DOA).
• Ensure timely delivery and closure of all COSA and audit recommendations.

Core Duties Include:
• Deliver assurance on centralised finance systems in line with corporate reform objectives.
• Maintain ownership and integrity of finance systems RACMs (Risk and Control Matrices).
• Support ongoing project delivery and change programmes involving finance systems.
• Liaise with internal governance and audit functions to demonstrate a mature controls environment.

Knowledge, Skills & Qualifications:
Knowledge of:
• Finance and IT systems audits
• Governance, risk, and control frameworks (e.g., COSA, MetricStream)
• Enterprise finance systems and related transformation programmes
• Corporate data governance principles
Skills:
• Strong leadership and stakeholder management across matrix environments
• Confident communicator with excellent influencing and presentation skills
• Experience in Lean or Six Sigma methodologies (desirable)
• Deep understanding of financial control environments and systems compliance
Qualifications:
• Fully qualified accountant (ACCA / CIMA or equivalent)

Morson is acting as an employment business in relation to this vacancy

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.

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.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.