Finance Analyst Apprenticeship

Summary

Are you looking to build a long-term career in Finance? Then look no further! Start your career today with a Level 3 apprenticeship with AVEVA and Kaplan. You will be gaining valuable workplace experience whilst working towards the Finance Analyst apprenticeship.

Wage

£24,000 a year

Training course

Data technician (level 3)

Hours

Monday to Friday, Hybrid working

37 hours a week

Start date

Monday 7 September 2026

Duration

1 year 6 months

Positions available

1

Work

Most of your apprenticeship is spent working. You’ll learn on the job by getting hands-on experience.

What you'll do at work

The main duties the apprentice will perform will vary based on the role of each particular rotation. Each apprentice will have a minimum of four rotations of six months in duration. Within 24 months (two years), the apprentice will have had a great range of experience in four different roles. Skills and knowledge gained during this time will include:

• Fundamentals of various standardised finance processes including Procure to Pay, Invoice to Cash, Record to Report etc.
• Knowledge of finance systems such as Oracle Fusion ERP and general business systems such as Microsoft Outlook, Excel, PowerPoint etc. 
• Understanding of compliance, controls and related processes in a global software company.

Where you'll work

High Cross, Madingley Road
Cambridge
CB3 0HB

Training

Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.

Training provider

KAPLAN FINANCIAL LIMITED

Training course

Data technician (level 3)

What you'll learn

• Select and migrate data from already identified sources.
• Format and save datasets.
• Summarise, analyse and explain gathered data.
• Combine data sets from multiple sources and present in format appropriate to the task.
• Use tools and/or apply basic statistical methods to identify trends and patterns in data.
• Identify faults and cleanse data to improve data quality, for example identifying gaps, duplicate entries, outliers and unusual variances, including cross-checking across data elements or between data sources.
• Audit data results for maintenance of data quality, reviewing a data set once all sources are combined, to ensure accuracy, completeness, consistency and traceability from original data.
• Demonstrate the different ways of communicating meaning from data in line with audience requirements.
• Produce clear and consistent documentation of the data provided to others and of actions completed. Where appropriate or mandated by the working context, this documentation should use standard organisational templates.
• Store, manage and distribute data in compliance with organisational, national, sector specific standards and or legislation.
• Considers sustainability and ways to reduce impact. For example, using cloud storage, sharing links to files, avoid storing multiple versions of files, and reducing the use of physical handouts of documentation.
• Parse data against standard formats, and test and assess confidence in the data and its integrity.
• Operate collaboratively in a working context that accounts for, and takes advantage of, the roles, skills and activities of others, especially those interacting with the same data sets or working towards a common goal.
• Prioritise own activities within the context of the duties to be performed, taking account of any known or expected impact on others.
• Follows equity, diversity and inclusion policies in the organisation for a common goal.
• Demonstrate the ability to use different tools and methods to formulate and utilise effective prompts to research, apply, and evaluate data transformation techniques.
• Select and migrate data from already identified sources.
• Format and save datasets.
• Summarise, analyse and explain gathered data.
• Combine data sets from multiple sources and present in format appropriate to the task.
• Use tools and/or apply basic statistical methods to identify trends and patterns in data.
• Identify faults and cleanse data to improve data quality, for example identifying gaps, duplicate entries, outliers and unusual variances, including cross-checking across data elements or between data sources.
• Audit data results for maintenance of data quality, reviewing a data set once all sources are combined, to ensure accuracy, completeness, consistency and traceability from original data.
• Demonstrate the different ways of communicating meaning from data in line with audience requirements.
• Produce clear and consistent documentation of the data provided to others and of actions completed. Where appropriate or mandated by the working context, this documentation should use standard organisational templates.
• Store, manage and distribute data in compliance with organisational, national, sector specific standards and or legislation.
• Considers sustainability and ways to reduce impact. For example, using cloud storage, sharing links to files, avoid storing multiple versions of files, and reducing the use of physical handouts of documentation.
• Parse data against standard formats, and test and assess confidence in the data and its integrity.
• Operate collaboratively in a working context that accounts for, and takes advantage of, the roles, skills and activities of others, especially those interacting with the same data sets or working towards a common goal.
• Prioritise own activities within the context of the duties to be performed, taking account of any known or expected impact on others.
• Follows equity, diversity and inclusion policies in the organisation for a common goal.
• Demonstrate the ability to use different tools and methods to formulate and utilise effective prompts to research, apply, and evaluate data transformation techniques.

Training schedule

Our Finance Analyst apprenticeship programme integrates eight modules of technical training with work-based projects. This ensures that learning and skills are directly applied to the apprentice’s role, and maximises the time used as part of off-the-job training.

As an evolution of the Data User apprenticeship, this industry-tailored programme is the first of its kind to combine essential data skills and competencies with core accountancy knowledge for those working in a finance function, but not necessarily in an accounting role.

It offers your employees the unique opportunity to be equipped with relevant skills to directly apply their learning in the workplace and unlock potential from your data. As well as developing technical skills using a range of core technologies and platforms, your employees will develop the ability to source, analyse, work with financial data, and generate insights that underpin business decisions to build a case for change.

More training information

Kaplan is one of the world’s largest and most diverse education providers. We are one of the UK’s leading training providers for finance and accountancy qualifications and train thousands of accountants each year.

We have been educating and training students for over 70 years and have helped thousands of people achieve their career goals. We offer apprenticeships from Level 2 to Level 7 and cover Accountancy & Tax, Banking & Finance and Data & IT.

We offer study methods to suit all types of learning, whether you want to be face-to-face, or left alone to study in your own time. 

Requirements

Essential qualifications

GCSE in:

• Maths (grade 9 - 5 )
• English (grade 9 - 5 )

Share if you have other relevant qualifications and industry experience. The apprenticeship can be adjusted to reflect what you already know.

Skills

• Communication skills
• IT skills
• Organisation skills
• Attention to detail
• Customer care skills
• Problem solving skills
• Presentation skills
• Administrative skills
• Number skills
• Analytical skills
• Logical
• Team working
• Initiative