In a world where ever-growing sources of business data become increasingly critical to maintaining competitive advantage, organisations are relying heavily on data to make decisions. There are now numerous data-specific roles across industries, but many people have misconceptions about them, particularly when it comes to understanding the differences between similar sounding roles like Data Analyst and Data Engineer, for example. 

For senior leaders tasked with the digital transformation of their organisation, understanding what these roles do and having clarity of the skills required for each role is key to hiring success.

Here we’ll consider the difference between Data Analysts and Data Engineers. The two roles work on different business priorities and require distinct skillsets. In this article, we’ll look at the job descriptions, organisational roles, required skill sets, and salary expectations for each of these exciting data careers.

What does a Data Analyst do?

In simple terms, a Data Analyst's mission is to comb through data and make it understandable. They transform what appears to be an incomprehensible tangled mess of numbers into actionable insights, in order for teams and organisations to reach decisions.

A Data Analyst holds an entry-level position on a data analytics team. Data Analysts need to be skilled at converting numerical data into a format that everyone in an organisation can understand. They must be proficient in a variety of areas, including programming languages such as Python, tools such as Power BI, and the fundamentals of data processing, reporting, and modelling. With enough experience, a Data Analyst can gradually advance to become a Data Scientist.

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Key skills of a Data Analyst

The backgrounds of Data Analysts vary greatly. A Data Analyst would traditionally have been someone with a bachelor's or master's degree in maths or computer science. The contemporary Data Analyst, can have a background from almost any field, however they still tend to come from a role which involves some quantitative element. The education needed to become a Data Analyst isn’t stringent, as the capacity to work with and understand data is much more important.

The essential skills of a good Data Analyst include:

• Expertise with data mining techniques
• Up-to-date knowledge with emerging technologies, machine learning and data frameworks
• Advanced analytical skills with sharp attention to detail
• Knowledge of Python (the most popular programming language for analytics)
• SQL experience

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What does a Data Engineer do?

A Data Engineer’s job is to build and maintain data architecture which ensures the creation of good quality data that can be used by the rest of the organisation. They are experts in the preparation of large datasets for use by Data Analysts. Where a Data Analyst must interpret data, an engineer must create programmes that can generate data into a meaningful configuration, such as making sure the format of the data is what the Analytics team requires. Data Engineers build the data warehouses, data pipelines, and databases that Data Analysts and data scientists use to access and manipulate data.

If we consider data as a tool, imagine that a Data Engineer is the person in charge of the factory, ensuring that all of the equipment is neatly organised so that everyone can locate the specific tool they require.

Key skills of a Data Engineer

Working with both structured and unstructured data is essential for a Data Engineer. As a result, SQL expertise is a critical requirement. A Data Engineer must also perform tasks such as data deduplication, data management, and data cleaning. Like Data Analysts, Data Engineers need to have strong programming skills and an understanding of algorithms. Creating an API, for example, or building a cloud infrastructure could be among their responsibilities. Where a Data Analyst’s role is primarily analytical, a Data Engineer’s role is a highly technical function that necessitates extensive knowledge of engineering and testing tools.

The essential skills of a good Data Engineer include:

• Hadoop, MapReduce, Pig, Hive, and Data Streaming experience
• Extensive knowledge of database systems, including SQL and NoSQL
• Deep understanding of software engineering and data systems
• Excellent communication skills in order to engage senior stakeholders, assess user requirements and translate this information into useful data products

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Data Analysts vs Data Engineers: Salary potential

If you've made it this far, you're probably wondering which role commands the highest salary. As with any job, a variety of factors such as geographical location, level of experience, company, and job role influence how much a Data Analyst or Data Engineer can expect to earn.

At the time of writing, according to Indeed.co.uk, the average base salary for a Data Engineer is £60,084 per year in the United Kingdom. Comparatively, the average base salary for a Data Analyst is £34,100 per year.

But keep in mind that these are merely averages, and factors such as breadth of knowledge, brand name, company size, profile, and location can cause a significant difference in salary between a Data Analyst and a Data Engineer.

Data Analyst vs Data Engineer: which skill set is key for your organisation?

If poor data quality is a current pain point in your organisation, it might be caused by a lack of data engineering talent to build and maintain the data pipelines. 

If your organisation has lots of data being produced but is struggling to inform decision-making with that data, then data analysts can solve this gap.

However, it’s important to note that any data-driven organisation will need both roles - the success of the analytics team is dependent on data engineering. The work of the data engineering team to produce quality data is maximised by a strong analytics team.

Upskill to become a Data Analyst

For entry-level learners keen to get started in data, at Cambridge Spark we offer a Level 4 Data Analyst apprenticeship. This programme teaches you to:

• Use Python programming and data analysis tools like Pandas and Numpy to generate insights that can be used to drive commercial decisions
• Work with various data formats, types, and databases, including SQL
• Use data visualisation to summarise, present, and make recommendations based on data analysis results
• Follow industry best practises and use collaborative workflows when testing and prototyping code in a production environment

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Upskill to become a Data Engineer

Cambridge Spark’s new Level 4 Data Engineer Apprenticeship provides learners with the technical and leadership skills needed to support business functions in the creation and maintenance of data analytics pipelines. Using the Level 4 Data Analyst apprenticeship standard, learners develop the skills required to improve data usability in their organisation. On successful completion of the apprenticeship, you’ll be able to:

• Understand the data engineering lifecycle and the role of a data engineer
• Develop the technical skills to create and maintain data analytics pipelines
• Understand the fundamentals of data modelling, its best practices and why it is important
• Effectively work with stakeholders to define and translate business requirements to a data model and data pipeline
• Train new talent and reskill existing employees with one of the most in-demand technical skill sets
• Develop key internal capabilities to increase the usability of critical datasets in your organisation

If you're interested in joining one of our programmes, please fill out the form below. One of our consultants will contact you with more information on how to get started.

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