Systems Accounting Manager

Cramond Bridge
10 months ago
Applications closed

Related Jobs

View all jobs

Finance Data Analyst

Finance Data Analyst

Senior Data Engineer

Systems and Data Architect

Systems and Data Analyst (Milton Keynes, ENG, GB, MK7 6AA)

Data Quality & Systems Manager

Join us as a Systems Accounting Manager

Join a team responsible for analysis and design solutions for a wide range of finance systems, operational accounting and processes and have end to end ownership for systems accounting and operational accounting models for Finance

You’ll support the organisation in the delivery of end-to-end financial control activity

We’re looking for someone to take on a new challenge, and put their analytical and problem-solving skills to good use

What you'll do

As Systems Accounting Manager, you’ll be accountable for the delivery and integrity of monthly, quarterly and annual financial information, including balance sheet, profit and loss account, and internal and external reporting. You'll also represent Finance on core strategic bank programmes.

Along with this, you’ll act as a key partner as you proactively contribute to decision making on complex or specialist issues, demonstrating judgement and a thorough understanding of the business.

Day to day, you’ll:

Act as an internal customer relationship manager and effectively maintain relationships with stakeholders

Complete value-added reporting and analysis to meet our customers’ needs

Work with the team and centres of excellence to standardise processes and outputs and to undertake continuous improvement activity

Use in-depth technical knowledge of transaction and product systems to support the development and design of robust, fit for purpose accounting solutions

Manage and deliver data and system solutions change executed within the appropriate governance framework to maintain data integrity and data quality standards throughout the change process

Explore and recommend architectural, process and operational accounting options whilst understanding the macro change environment

The skills you'll need

To succeed in this role, you’ll bring a wealth of knowledge and experience in financial control, preferably gained within financial services. You’ll have worked in a global context and will have the ability to build and maintain strong working relationships with a variety of stakeholders in a changing environment. And you’ll hold a professional accounting qualification or have significant relevant experience in place of this.

You'll have experience in developing operating models, operational accounting and systems architecture solutions drawing on specific development skills and wider system experience.

We’ll also look to you to bring:

Practical experience in developing and documenting the end to end accounting models for Finance

A proven ability to build strong working relationships with a variety of stakeholders and customers across organisations and geographical boundaries, influencing and challenging as required

Up to date knowledge of accounting standards

Strong communication skills and the ability to clearly and succinctly articulate issues to senior management

Demonstrable commitment to continuous improvement activity

An astute eye for detail, with excellent analytical and problem-solving skills

Detailed knowledge of the end to end accounting models form source platforms to the General Ledger

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.