Finance Business Partner

Sudbury
9 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst - E-Commerce

Data Analyst - E-Commerce

Business / Data Analyst

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Data Analyst - Farming Operations

Job Title: Finance Business Partner
Location: Sudbury
Contract Type: Full-time
Salary: Competitive + Benefits

Are you a strategic thinker with a passion for driving performance and leading high-performing teams?

We're looking for an experienced Finance Business Partner to join a dynamic leadership team and play a pivotal role in shaping the future of a forward-thinking organisation.

This is a fantastic opportunity for a finance professional who thrives in a collaborative environment, enjoys variety in their role, and has a keen eye for continuous improvement. You'll be the bridge between finance and business operations-translating numbers into actionable insights that fuel growth and innovation.

Key Responsibilities:
• Act as a key contributor to the organisation's strategic direction as part of the Leadership Team.
• Lead, mentor, and develop the finance, payroll, and HR administration teams-building capability and promoting cross-functional collaboration.
• Analyse and present key business intelligence to inform decisions, drive performance, and identify opportunities for efficiency.
• Own the finance annual plan, budget setting, costings, and monthly reporting cycles.
• Provide financial analysis and robust business cases to support commercial initiatives.
• Manage and improve financial disciplines, cost control, and risk management across the organisation.
• Coordinate year-end audit activity and ensure full compliance with statutory and internal reporting requirements.
• As Company Secretary, ensure governance responsibilities are met and maintain all statutory records.
• Oversee payroll, pensions, HR records, and compliance with time & attendance systems.
• Work closely with senior stakeholders and provide strategic financial leadership at all levels.

What We're Looking For:

Essential:
• 7-10 years of finance experience, ideally in a manufacturing or product-based environment.
• At least 5 years' experience in a leadership or managerial finance role.
• Strong analytical skills with a proactive, solutions-focused mindset.
• Advanced knowledge of Excel, ERP systems, and financial modelling.
• Excellent communication skills with the ability to influence at all levels.
• Commercially astute with solid decision-making and problem-solving abilities.

Desirable:
• Degree in Finance, Business Administration, or a professional accounting qualification (e.g. ACCA, CIMA, ACA).
• Experience in payroll and HR administration.
• Industry experience or similar manufacturing environments.

Why Join Us?
You'll be part of an exciting, values-driven organisation where your input matters. They offer a supportive environment that encourages personal development, innovation, and continuous improvement. This is a leadership role with real impact, offering you the platform to drive change and make a difference

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 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.

Jobs

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.

Education

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.

🍪 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.