Data & AI Consultant Apprenticeship

Summary

Entry-level Data & AI Consultant role for individuals looking to build a career in data engineering and AI.

You will work on real client projects, supporting the design and delivery of data pipelines, models, dashboards, and AI-driven solutions.

This is not a passive learning role, you will be expected to contribute quickly.

Wage

£15,600 for your first year, then could increase depending on your age

National Minimum Wage rate for apprentices

Training course

Data technician (level 3)

Hours

Monday to Friday, 9.00am to 5.00pm.

37 hours 30 minutes a week

Start date

Wednesday 1 July 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

• Build and support data pipelines (ETL/ELT)
• Work with data across Bronze / Silver / Gold layers
• Transform and model data for reporting and analytics
• Support development of Power BI dashboards
• Assist in AI use cases (automation, simple models, insights)
• Work within Azure-based environments (Databricks, Data Lake, etc.)
• Test, validate, and document solutions

Where you'll work

Unit 1
160 West End Lane
London
NW6 1HU

Training

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

Training provider

LEARNING FOR FUTURES LTD

Training course

Data technician (level 3)

What you'll learn

Course contents

• 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

This training schedule has not been finalised. Check with this employer if you’ll need to travel to a college or training location for this apprenticeship.

Requirements

Essential qualifications

GCSE in:

Maths & English (grade Grade A-C or 4-9)

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
• Attention to detail
• Organisation skills
• Customer care skills
• Problem solving skills
• Administrative skills
• Analytical skills
• Logical
• Team working
• Creative
• Initiative
• Patience

Other requirements

Nice to Have:

• Basic SQL / Python / Excel
• Any self-learning in data or AI
• Exposure to Power BI or dashboards