Degree Apprentice - Data Analytics Engineer
Yunex Traffic
ENGLAND (BH23 6BU)
Closes on Thursday 31 December 2026
Posted on 9 November 2026
Contents
Summary
You'll gain real-world experience, work towards a degree qualification and develop the skills needed to build a successful career, all while earning a salary. You'll combine practical workplace experience with academic study, enabling you to apply your learning directly to real projects and business challenges.
- Wage
-
£22,000 a year
- Training course
- Machine learning engineer (level 6)
- Hours
-
Monday to Friday, 9.00am to 5.00pm. Hybrid working role where you may be able to work from home for 2 days a week.
37 hours 30 minutes a week
- Start date
-
Tuesday 31 August 2027
- Duration
-
4 years
- 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
What you’ll be doing:
- Data Engineering & Integration – Build and maintain data pipelines that ingest, transform and integrate data from business systems, APIs and other sources
- AWS Data Platforms – Develop and support cloud data solutions using services such as S3, Glue, Redshift, Redshift Spectrum, Lambda and IAM
- Data Transformation – Use SQL and Python to transform, validate and prepare data for reporting, analytics and downstream applications
- Data Modelling – Design and maintain robust data models, including dimensional models, fact and dimension structures, relationships and reusable business measures
- Power BI & Semantic Modelling – Develop Power BI semantic models, reports and dashboards using Power Query, DAX and modern data visualisation techniques
- Thick/Thin Analytics Architecture – Help develop governed, reusable semantic models that provide consistent business logic and measures to multiple lightweight Power BI reports
- Data Quality – Build validation, reconciliation and data-quality controls to ensure information is accurate, complete, consistent and trustworthy
- Data Management & Governance – Support good data-management practices including metadata, data lineage, data definitions, ownership, classification and lifecycle management
Security & Access – Apply appropriate security controls across - AWS and Power BI, including IAM, least privilege, encryption and row-level security
- Automation & Deployment – Use Git, CI/CD and Infrastructure as Code tools such as Terraform to support controlled, repeatable development and deployment
- Monitoring & Optimisation – Monitor and troubleshoot data pipelines and reporting solutions, improving reliability, performance, scalability and cost efficiency
Where you'll work
1 VISCOUNT ROAD AVIATION BUS
BOURNEMOUTH INTERNATIONAL AI
CHRISTCHURCH
ENGLAND
BH23 6BU
Training
Apprenticeships include time away from working for specialist training. You’ll study to gain professional knowledge and skills.
Training provider
CAMBRIDGE SPARK LIMITED
Training course
Machine learning engineer (level 6)
Understanding apprenticeship levels (opens in new tab)
What you'll learn
Course contents
- Assess vulnerabilities of the proposed design, to ensure that security considerations are built in from inception and throughout the development process.
- Translate business needs and technical problems to scope machine learning engineering solutions.
- Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution.
- Apply methodologies and project management techniques for the machine learning activities.
- Create and deploy models to produce machine learning solutions.
- Document the creation, operation and lifecycle management of assets during the model lifecycle.
- Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness.
- Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security.
- Refine or re-engineer the model to improve solution performance.
- Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable.
- Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process.
- Apply machine learning and data science techniques to solve complex business problems.
- Track and test continual learning models.
- Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements.
- Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome.
- Transition prototypes into the live environment.
- Complete audit activities in compliance with policies, governance, industry regulation and standards.
- Consider the risks with using digital and physical supply chains.
- Ensure the model capacity is scaled in proportion to the operating requirements.
- Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced.
- Monitor data curation and data quality controls including for synthetic data.
- Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources.
- Identify and embed changes in work to deliver sustainable outcomes.
- Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability.
- Develop a process to decommission assets in line with policy and procedures. Manage current and legacy models in line with industry approaches.
- Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.
- Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales.
- Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
- Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation.
- Comply with equality, diversity, and inclusion policies and procedures in the workplace.
- Horizon scan to identify new technological developments that offer increased performance of data products.
- Apply Machine Learning principles and standards such as, organisational policies, procedures or professional body requirements.
- Integrate AI-based approaches, including those provided by third-party vendors’ Application Programming Interfaces, into existing and new processes.
- Proactive identification of the potential for automation for example through AI solutions embedded within tooling.
- Assess vulnerabilities of the proposed design, to ensure that security considerations are built in from inception and throughout the development process.
- Translate business needs and technical problems to scope machine learning engineering solutions.
- Select and engineer data sets, algorithms and modelling techniques required to develop the machine learning solution.
- Apply methodologies and project management techniques for the machine learning activities.
- Create and deploy models to produce machine learning solutions.
- Document the creation, operation and lifecycle management of assets during the model lifecycle.
- Apply techniques for output model testing and tuning to assess accuracy, fit, validity and robustness.
- Assess system vulnerabilities and mitigate the threats or risks to assets, data and cyber security.
- Refine or re-engineer the model to improve solution performance.
- Apply techniques for monitoring models in the live environment to check they remain fit for purpose and stable.
- Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process.
- Apply machine learning and data science techniques to solve complex business problems.
- Track and test continual learning models.
- Analyse test data, interpret results and evaluate the suitability of proposed solutions both new and inherited models, considering current and future business requirements.
- Identify, consider and advocate for ML solutions to deliver an environmental and operational sustainable outcome.
- Transition prototypes into the live environment.
- Complete audit activities in compliance with policies, governance, industry regulation and standards.
- Consider the risks with using digital and physical supply chains.
- Ensure the model capacity is scaled in proportion to the operating requirements.
- Support the evaluation and validation of machine learning models and statistical evidence to minimise algorithmic bias being introduced.
- Monitor data curation and data quality controls including for synthetic data.
- Identify and select the machine learning or artificial intelligence platform architecture and specific hardware, to contribute to solving a computational problem using allocated resources.
- Identify and embed changes in work to deliver sustainable outcomes.
- Monitor model data drift, using performance metrics to ensure systems are robust when moving outside of their domain of applicability.
- Develop a process to decommission assets in line with policy and procedures. Manage current and legacy models in line with industry approaches.
- Undertake independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances.
- Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers and multi-disciplinary teams with conflicting priorities, interests and timescales.
- Produce and maintain technical documentation explaining the data product, that meets organisational, technical and non-technical user requirements, retaining critical information.
- Create and disseminate reports, presentations and other documentation that details the model development to confirm stakeholder approval for handover to implementation.
- Comply with equality, diversity, and inclusion policies and procedures in the workplace.
- Horizon scan to identify new technological developments that offer increased performance of data products.
- Apply Machine Learning principles and standards such as, organisational policies, procedures or professional body requirements.
- Integrate AI-based approaches, including those provided by third-party vendors’ Application Programming Interfaces, into existing and new processes.
- Proactive identification of the potential for automation for example through AI solutions embedded within tooling.
Training schedule
- Part of being an apprentice at Yunex Traffic means you get to work on real life projects whilst also being supported on a guided learning journey
- This will be a 4 -year apprenticeship where you will be put onto the Machine Learning standard with a pathway into Software
- You will virtually attend Cambridge Spark on a 1-day a week basis with check ins and sessions each quarter
Requirements
Essential qualifications
GCSE in:
- English (grade 4)
- Maths (grade 4)
A Level in:
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
- Problem solving skills
- Presentation skills
- Number skills
- Analytical skills
- Logical
- Team working
- Curiosity
- Data Management
- Inquisitive
Other requirements
Yunex Traffic recognise that building a diverse workforce is critical to the success of our business. We strongly encourage applications from a diverse talent pool and welcome the opportunity to discuss flexibility requirements and workplace adjustments with all our applicants to encourage agile working and innovation.
About this employer
We operate in 24 countries around the world and make infrastructure permanently environmentally friendly and efficient with cutting-edge digital technology in hundreds of cities. Our smart mobility solutions are currently deployed in major cities around the world, including Dubai, London, Berlin, Bogota and Miami. We have 3100+ employees from 58 nations in our 24 offices all over the world.
https://www.yunextraffic.com/ (opens in new tab)
Company benefits
Benefits include: 26 days holiday, increasing up to 29 days with length of service. Excellent pension. Annual bonus. Flexible benefits to suit your personal needs. Investment in personal development.
Disability Confident
A fair proportion of interviews for this apprenticeship will be offered to applicants with a disability or long-term health condition. This includes non-visible disabilities and conditions.
You can choose to be considered for an interview under the Disability Confident scheme. You’ll need to meet the essential requirements to be considered for an interview.
After this apprenticeship
Your earnings can increase over time with an apprenticeship. Find out about potential future pay (opens in new tab).
- Permanent employment after apprenticeship in out DevOps Team
Ask a question
The contact for this apprenticeship is:
CAMBRIDGE SPARK LIMITED
ukcareers@yunextraffic.com
The reference code for this apprenticeship is VAC2000063233.
Apply now
Closes on Thursday 31 December 2026
After signing in, you’ll apply for this apprenticeship on the company's website.