When you fail to make data-driven decisions, you leave money on the table and allow those who can make strategic decisions to gain higher profit margins. But all it takes to avoid this scenario is a Data Engineer, because their job is to build the data warehouses, data pipelines, and databases, which enable your Data Analysts and Data Scientists to access and use data.
Data Engineers give you the tools to thrive and help your organisation make smarter, faster decisions that support data-driven transformation, underpin your AI initiatives, and enable you to become a competitive powerhouse.
But there’s a problem.
Nearly 9 in 10 (87%) organisations either currently have a skills gap or expect to have one in the next few years, which is costing their business 8.5% of their annual revenue. And the problem doesn’t look like it’s going anywhere, any time soon – projections by Randstad show that by 2030, there will be 10.5 million unfulfilled vacancies for Data Engineers worldwide.
Why Businesses Need Data Engineers
Fail to get on top of your data engineering needs now, and you’ll struggle to keep pace with change. For example, without a Data Engineer, your business is likely to have a data swamp because there’s so much legacy data, no one knows how much there is or what’s in it, they don’t know where specific information is located, or who has access to it.
This makes the data messy, so queries are slow. And because there’s no Data Engineer to build data pipelines, everyone’s left to their own devices to build what they need. Meaning you end up operating amongst confusion, because there are multiple dashboards showing different information, and the numbers just don’t add up.
WITHOUT data engineering skills in your organisation, you risk poor quality data and operational silos that ultimately impact your ability to make good decisions, which affects your performance and competitiveness.
WITH data engineering skills in your organisation, you stand to gain valuable insights from your data:
Data Engineers Ensure Data Quality and Reliability
To make good decisions, you need good data. A Data Engineer ensures your data is:
Accurate: error-free and reflects real-world information
Complete: no missing values
Consistent: in the same format and structure across all platforms and databases
Timely: updated regularly
Reliable: maintained over time to ensure trustworthiness
Data Engineers ensure data quality by designing and managing systems that use rules to avoid duplicate, inaccurate, or corrupted information, as well as implement regular checks to ensure data remains reliable and aligned with business controls.
Data Engineers Build and Maintain Robust Data Pipelines
With good quality data in hand, your Data Engineer can start to build pipelines and workflows that support decision making. Automations enable the business to extract immediate value from actionable data. Analytics helps the business learn how to optimise its operations, based on past success/failure, and predict outcomes. And streamlining processes allows the business to handle larger data sets, as well as enhance outcomes to minimise errors and boost productivity.
Data Engineers Make Data Accessible for All Users
Data Engineers are instrumental to democratising data. First, they are accountable for collating data from disparate sources and making it available through centralised architectures, such as a data warehouse, data lake, or hybrid lakehouse. Then, they are responsible for maintaining data quality, optimising data architectures, controlling access and security, and liaising with key stakeholders to understand the organisation’s data requirements. Only then can they build the tools that enable business users (both technical and non-technical) to ‘self-serve’.
So, who are the key players a Data Engineer supports?
Data Architect
Depending on the size of your organisation, you may have a Data Architect as well as a Data Engineer. While the two roles have some overlap, the important difference is that while the Architect dictates how data is collected, stored, and made available to others, the Engineer is responsible for creating, implementing, and maintaining the data pipelines in accordance with the Architect’s plan.
It requires close collaboration to ensure the availability of data continues to meet the needs of the organisation.
Data Analysts
Once the Data Engineer has structured data within the organisation and made it accessible, Data Analysts can start to query the data. Their focus is on day-to-day business, creating models and algorithms that address a specific business problem – for example, ‘what can we do in the next 90 days to improve our CSAT scores?’ or ‘what changes would reduce our wastage by 10% in the next year?’.
Because the Data Engineer has ensured the data used is of good quality, the Data Analyst can trust the outputs to be reliable.
Depending on the size of your organisation, you may have a single person or a small team of analysts, or a whole department with specialist roles, such as product analyst, engineering analyst, sales analyst, marketing analyst, and operations analyst. In any case, the Analysts are unable to perform their role until the Data Engineer has laid the right foundations.
Data Scientists
In contrast to a Data Engineer, who tends to possess general skills, a Data Scientist is highly skilled in a specific area, like statistics or AI. Like an Analyst, a Data Scientist will access data to query the business. But while an Analyst is focused on today, a Data Scientist is more strategic, looking at the bigger picture, using historical data to predict future trends, and evaluating the outcomes of taking certain actions. They will have direct lines of report to the board to help guide how the business needs to adapt to the shifting landscape.
Future-Proofing Your Team with the Right Skills
University Degree vs. Apprenticeship Programme
Nearly all (93%) CEOs who introduce upskilling programmes observe an increase in productivity, improve their ability to attract and retain talent, and build a more resilient workforce. There are two main learning pathways to upskill your workforce: a university degree and an apprenticeship programme.
Both will help you to build a future talent pipeline by equipping your team with the (transferrable) skills and knowledge they need to interact with data.
However. While there are specific apprenticeship programmes dedicated to data engineering, the same is not true of university degrees. A search on the UCAS website returns 479 related courses, but only one has a strong focus on data engineering: Computer Science (Data Engineering).
University degrees are purposefully designed to give learners a broader academic foundation to allow for more diverse career options upon graduation. In contrast, an apprenticeship programme favours hands-on, practical training to prepare learners for a specific career.
The following sections compare the Computer Science (Data Engineering) university degree with the Cambridge Spark Data Engineer Apprenticeship (L5).
Focus
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University Degree |
Apprenticeship |
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The emphasis is placed on theory to develop learners into subject matter experts with comprehensive knowledge of the field. The course is 100% academic study, with the option of a placement year. |
The emphasis is on applied learning, to allow learners to put their new skills into practice on day one of their programme. The course is 20% theoretical learning via live lectures, workshops, and self-paced e-learning, and 80% off-the-job training. |
Learning Support
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University Degree |
Apprenticeship |
|
Learners have access to personal tutoring, academic skills, peer-led support sessions, and tailored field trips. |
Learners are supported by expert lecturers for theoretical learning, technical mentors who help put the theory into practice, and professionally trained coaches who link learning back to the role. In addition, learners can access EDUKATE.AI, an online learning platform that requires no IT set up, and uses real datasets in a sandbox environment, so learners can practice and hone their skills to accelerate training outcomes. |
Networking Opportunities
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University Degree |
Apprenticeship |
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Learners can network with others on their course, and are encouraged to participate in peer-to-peer support sessions. |
On day one of the programme, learners are invited to join a community of 4,000+ learners and alumni. |
Suitability
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University Degree |
Apprenticeship |
|
It’s seen as the traditional route for school leavers, which encourages younger cohorts. For some mature learners, this can feel isolating, because they’re studying alongside those who are at a different life stage. |
During the last academic year, under-19s accounted for less than a quarter (23.2%) of all learners enrolled on apprenticeship programmes - indicating this training pathway is fit for those looking to upskill, as well as those entering the workforce. |
Costs
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University Degree |
Apprenticeship |
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The Computer Science (Data Engineering) degree costs £9,535 per year (£28,605 for a 3-year course). If the organisation does not cover the cost in full or splits the costs with the individual, it may require the learner to take out a loan to pay the tuition fees. |
The Data Engineer Apprenticeship (L5) can be funded using the Apprenticeship Levy. If your business does not pay the Levy, you are required to pay just 5% of the total cost. |
Time Commitment
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University Degree |
Apprenticeship |
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This is a 3-year, full-time degree. |
A 14-month programme with a 6-hour minimum off-the-job learning commitment per week. |
Location
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University Degree |
Apprenticeship |
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Learning takes place on the university’s campus. The intake commences annually in September. |
Off-the-job training takes place at the learner’s place of work, with additional study at home. Rolling intakes with multiple cohorts annually. In addition, for companies wanting to upskill an entire team, a special intake can be organised just for them. |
Application Process
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University Degree |
Apprenticeship |
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To apply, learners must have accrued 112 UCAS points from A-level study. Additionally, there is no guarantee that learners will be accepted onto their first choice of course. |
Learners do not require previous experience or training. They do need to be employed in England and resident in the UK or EEA for the last 3 years. And work at least 30 hours a week. |
Upskill Your Workforce With Data Engineering Expertise
At Cambridge Spark, we set the gold standard. We’re always first to market with recognised certifications and qualifications that develop new skills. Enrol your learners onto our Data Engineering Apprenticeship (L5) and you empower them to:
- Understand the data engineering lifecycle and data modelling
- Create and maintain data analytics pipelines to deliver valuable insights
- Maximise the value of business data
- Build the core technical and leadership skills that support the data-driven transformation of your organisation.
To date, we have generated £350m+ in ROI for our clients because our apprenticeship programmes are designed to deliver real business impact.
Discover more about the Data Engineer Apprenticeship (L5) and enrol now.