Data Engineer Level 5

Level 5 Apprenticeship

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Introducing QA’s brand new Level 5 Data Engineer apprenticeship programme, meticulously designed to provide learners with a strong foundation for the development of advanced technical competencies, enabling comprehensive professional and personal growth.

This specialised apprenticeship curriculum comprehensively covers essential knowledge and skills crucial for the proficient design, development and management of intricate data systems.

Learners will be equipped to skilfully architect, administer, and transform data into actionable insights tailored for consumption by Data Scientists, Data Analysts, and Business Intelligence professionals, empowering organisations to drive innovation, optimise business processes, and catalyse informed decision-making.

A natural progression having completed this programme would be onto our DTSL6 Degree Apprenticeship Programme data pathway.

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Summary

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About this course

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Careers

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Modules

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Learner support

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Entry info & fees

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What makes our programme different?

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Enquire now

Fees

This programme is fully funded by your employer through the Apprenticeship Levy

Level of Study

Level 5 Apprenticeship

Apprenticeship Standard

Data Engineer L5

Apprenticeship Certificate

Option to achieve DP-203: Data Engineering on Microsoft Azure

Entry Requirements

A Level 3 qualification in a relevant area in any grade. Find out more

English Language Requirements

GCSE at Grade C, or equivalent

Mode Of Study

Part-Time, Blended and Work-based Learning

Duration

21 months including EPA (typically 4 months)

Assessment Methods

On-programme activities, work-based apply tasks, major work-based project, work-based portfolio, End Point Assessment (exam for optional DP-203)

Locations

Live online learning (face-to-face for closed cohorts only, dependent upon numbers and location - London, Birmingham, Manchester, Leeds)

Tools and tech covered in programme

Python, Pyspark, SQL, SQL Server – TSQL, MongoDB, Cassandra, Synapse Analytics, Apache: Spark, Airflow, Kafka & Flink, Azure Data Factory, Kubernetes, PowerBI, Quicksight, Git, AWS, MS Azure, Docker, Terraform.

PLEASE NOTE: To be eligible for a degree apprenticeship programme, learners must be:

  1. currently in full-time employment and based in the UK
  2. interested in completing a degree apprenticeship with their current employer.

 

data engineer learner  journey

This apprenticeship is designed to produce Data Engineers with the skills to build systems which collect, manage, and convert data into valuable information for data scientists, data analysts and business intelligence analysts to interpret and translate into business impact.

The Data Engineer L5 apprenticeship blends online learning, face-to-face workshops and on-the-job experience to transform learners into highly skilled tech professionals.

Who is it a good fit for?

  • Existing Data Engineers looking to upskill.
  • Data Analysts looking to move into an Engineering role.
  • Database Administrators.

As part of their programme learners will complete:

  • 7 learning knowledge modules teaching theory and practical application. These are primarily taught online and are supported by classroom training workshops.
  • Work-based portfolios & projects will be completed at work, over the course of the programme to demonstrate practical abilities.

Optional Microsoft Certification

As part of the Level 5 Data Engineer Apprenticeship, learners will be able to access Cloud Academy resources which will prepare them for taking the optional Microsoft Certification exam (DP-203 Data Engineering on Microsoft Azure).

The Programme provides academic qualification and professional experience tailored for Data Engineering roles.

Once learners have completed the L5 Apprenticeship, they will be a highly competent and knowledgeable member of their organisation contributing to building, optimising and maintaining systems that collect, manage, and convert data into usable information for stakeholders to interpret.

There is potential to progress to Data Scientist, Machine Learning Engineer or Software Engineer (with further training).

Technical Modules

Fundamentals of Data Engineering

This module provides a comprehensive understanding of the fundamentals of Data Engineering and encompasses:

  • The role's significance
  • Data types
  • Diverse sources
  • Data structures
  • Serialisation techniques
  • Compression methods
  • Modelling
  • Normalization benefits
  • De-normalization rationale
  • Data quality assurance
  • Frameworks
  • Risks
  • Storage solutions
  • Lifecycles
  • Ethical practices
  • Secure handling
  • Tool usage
  • Analytics applications.

Data Storage and Management

In this module, learners will explore data storage fundamentals, including RDBMS such as: SQL Server, SQL Fundamentals, NoSQL databases, data modelling, distributed systems, and cloud implementation.

Hands-on sessions cover database design, access management, issue communication, and querying data with SQL.

Data Processing, Transformation and ETL

This module covers:

  • Data processing and transformation
  • Python tools (NumPy, Pandas, SciPy)
  • Data quality assessment
  • Cleansing
  • Integration
  • Batch vs real-time processing
  • Spark
  • ETL/ELT
  • Cloud platforms
  • Azure data engineering
  • Pipeline orchestration
  • CI/CD
  • Hands-on data cleaning with Python libraries.

Planning a Data Engineering Product

This module explores the end-to-end process of developing data products. It begins with identifying business needs and evaluating requirements, then focuses on agile frameworks for user requirements and data product design. Best practices in software development are emphasised alongside gap analysis in existing tools.

Planning encompasses testing, data quality, automation, and risk analysis, with considerations for cost, organisational strategies, net-zero technologies, governance frameworks, compliance, security, and scalability.

Data product design, tools, and technologies are explored, including version control systems. Principles of sustainable data products, documentation, standardisation, SDLC, CI/CD pipelines, and data movement methods are covered within an organisational architecture context.

The workplace project involves ‘a significant and defined piece of work’ with real business impact. The business task agreed upon will be relevant for both organisational context and the apprentice’s role, and demonstrate the required skills and knowledge. The project will be completed whilst on programme and prior to End Point Assessment, typically at the place of work.

Data Engineering Product Development

This module will cover all the necessary knowledge and skills needed to build and test data products.

Topics include structured data extraction, integration platforms, data ingestion optimisation, automation of pipelines, testing, security, user requirements, interfaces, prototype evaluation, and stakeholder collaboration, emphasising continuous improvement, sustainable product development, and version control.

Data Operations

This module introduces learners to Data Operations, covering:

  • Technology service management
  • Incident response
  • Troubleshooting
  • Release and deployment methods
  • Data pipeline deployment and management
  • Monitoring
  • Continuous improvement
  • Data product evaluation
  • Quality assurance
  • Root cause investigation
  • Forecasting tools
  • Machine learning model development
  • Data governance
  • Further development aligned with operational best practices.

Emerging Tech and Personal Development

This module will introduce learners to the future of data engineering, this module explores emerging technologies and strategies shaping data management and analysis, empowering personal development through innovative data-driven approaches and insights.

Digital Learning Consultant

Your Digital Learning Consultant will be your primary contact, supporting you in the successful progression and completion of your apprenticeship.

Your Digital Learning Consultant will support you in reviewing your progress and collecting evidence of your practice at work to integrate into your module assessments and final endpoint project/assessment. They are also a point of contact for queries, concerns, or general support.

Your Digital Learning Consultant can help you with:

  • Coaching and supporting work-based learning activities.
  • Reviewing your progress with your apprenticeship portfolio progress.
  • Help with achieving your EPA Advice and guidance on mitigating (extenuating) circumstances processes and potential breaks in learning.

Workplace Mentor

A Workplace Mentor will be appointed by your employer and typically would be someone you work with. Your workplace mentor will be familiar with the apprenticeship programme and its workplace requirements. They will facilitate the workplace learning opportunities to enable you to meet the requirements of the degree apprenticeship standard.

QA Welfare Services

Our Student Welfare Team is on hand to assist you throughout your studies. Some apprenticeship learners have additional learning needs which the Welfare Team can assist with, or they might help you with personal circumstances that are affecting your studies.

Entry requirements for this programme are as follows:

A Level 3 qualification in a relevant area in any grade.

Acceptable qualifications include, any of:

  • Two A levels in one or more similar subject.*
  • Level 3 apprenticeship in a similar subject.
  • International Baccalaureate at Level 3 in a similar subject.
  • BTEC Extended Diploma in a similar subject.
  • Experience with programming languages (such as Python)
    OR equivalent work experience:
  • 2-3 years in a similar subject-related role.

*'Similar subject' relates to areas directly relevant to or commensurate with 'Digital and Technology Solutions'. Typically this would be areas such as but not limited to Level 3 digital apprenticeships, A-Level/BTEC Computer Science, Information Technology, Networking, Software Engineering, etc.

Please note: Learners must not hold an existing qualification at the same or higher level than this apprenticeship in a similar subject.

Fees & finance

There is no cost to you as an apprentice. Apprenticeships are fully funded through your employer.

If you are an employer, the maximum funding for this programme is £18,000. Expenses for travel to QA centres should be covered by the employer.

Data Expertise and Cloud Academy

  • 37,000 delegates on Data programmes and courses over 30 years.
  • 6 apprenticeships and 150 learning paths in data across our portfolio.
  • Over 40,000 hours of online self-paced training including protected, realistic sandbox environments to test skills.
  • Over 1,000 cloud and coding labs, 100 AI lab and courses and over 60 certification paths.
  • Real time feedback on labs and assessments. Using item response theory and AI we adjust learning assessments based on live progress for real time skill tracking.
  • Continued access to all the above for 12 months post programme completion.

Futureproofing and quality content

  • Additional machine learning module learning, GenAI and large language models equipping businesses for data readiness for the future
  • Established partnerships with all major vendors recognising QA as a valued training partner. Free exam vouchers across both Azure and AWS learning with this programme
  • HE partner network with Roehampton, Northumbria and Solent Universities offering degree –awarded Data apprenticeships up to BSc and MSc, for progression opportunities onto our AI Level 7 apprenticeship.

Learner support

  • 50 data coaches in our community.
  • DE5 included 23 days of tutor time in small groups, under guidance of a technical expert.
  • 98% pass rate in data. apprenticeships proves our focus on mapping for learner success.
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