Data engineering involves turning raw data into usable information for data scientists and others within an organisation. The work typically consists of designing and building systems for collecting, storing, and analysing data at scale. Working with data in this context encompasses a multitude of specialities within the field of data science, and it has applications in just about every industry.
Without data engineering, it would not be possible for businesses to make sense of the huge amounts of data available to them. This guide will explain more about who oversees data engineering within an organisation, why it is important it happens, and tell you how you can make a career move into the position.
What is a data engineer?
Data engineers are dedicated specialists, responsible for building the systems that will collect, manage, and convert raw data into useful information. This information will then be used and interpreted by the organisationās data scientists and business analysts. From this, predictions about the future can be made for the organisation, and decision-makers can make decisions for the business.
In a typical day, a data engineer will be responsible for:
- Acquiring data sets that align with the needs of the business
- Developing algorithms to transform data into actionable information
- Building, testing, and maintaining database pipeline architectures
- Collaborating with management to understand the organisationās objectives
- Creating data validation methods and data analysis tools
- Ensuring compliance with data governance and security policies
Data platform engineers
A data platform engineer is a specialist who builds and manages the environment that data engineers operate in. They improve the technical landscape and the effectiveness of the data platforms, allowing engineers to create scalable data pipelines, deploy models in production, and perform data discovery and metadata management.
The data pipeline
The data pipeline has four key stages that the data engineer will handle directly:
- Ingestion: the process of gathering data. This task can be focused or large-scale, depending on the number of data sources.
- Processing: ingested data is sorted to achieve a specific set of data to be analysed. This will commonly be done using a distributed computing platform for scalability when the data sets are large.
- Storing: this is the action of taking the results of the processing and saving the data for quick, convenient retrieval. The effectiveness of this relies on having a sound database management system. This can either be on-site or in the Cloud.
- Access: once the data is in place, it will be available to users with access.
Data engineering vs data science: the differences
Data engineers and data scientists are often confused with each other because the knowledge, skills, and education needed for both roles overlap. Despite this, the positions are not the same and there are important differences to note:
- Data engineers develop, test, and maintain data pipelines and architectures that a data scientist will use for analysis. The data engineer is responsible for the work which allows the data scientist to provide accurate metrics.
- Data scientists, on the other hand, use more advanced data techniques to make predictions about the future. They may automate their own machine learning algorithms or design predictive modelling processes that can handle both structured and unstructured data.
Data engineering vs data analytics: the differences
There is also confusion between the professions of data engineering and data analytics. A data analyst is responsible for making sense of existing data to solve tangible business problems using different tools and programming languages, data visualisation software, and statistical analysis. The role is more similar to that of a data scientist than a data engineer.
Why is data engineering important?
If an organisation does not have a functioning data engineering strategy, there is almost no point in collecting the data at all because it will not be of any use. The process of data engineering simplifies multiple sources of data, making it more reliable and useful for data scientists to work with.
This is especially important when considering big data. Organisations have access to vast amounts of data from both the real world and the digital sphere. This can be extremely beneficial, but it can also lead to information overload. The result of this is scattered data, which could prevent a company from drawing relevant insights.
Without these insights, the company will not be able to see a clear picture of its business functions. Management and decision-makers will therefore not be able to make the right business decisions without data engineering.
Why consider becoming a data engineer?
There has been a huge demand in recent years for employees to fill positions in data, including data engineering roles. A report from the US in 2020 even found that data engineer was the fastest-growing tech occupation, beating out data scientists. These trends are not likely to change, either; as long as organisations continue to use data to draw their insights and to make their decisions, data engineers will always be in demand.
Alongside this, rapid digital transformations post-pandemic have resulted in a skyrocketing amount of data available. This, by itself, has also created a boost in demand for data engineers.
Salary for a data engineer in the UK
The salary for a data engineer in the UK can vary, depending on the level of seniority, skill and experience, and even the location of the organisation in question. The national average is somewhere between Ā£49,000 and Ā£65,000 annually. If you would like to see a broader range of salaries for data engineers, please see our page.
How to become a data engineer
Becoming a data engineer requires a strong background in technology and a particular set of skills. Below is a breakdown of the typical skills required and the routes you can take to become a data engineer.
A data engineer skill set
Any role in data engineering will require at least basic knowledge on:
- Coding: this is essential to the role, and common programming languages involved include SQL, NoSQL, Python, Java, R, and Scala.
- Relational and non-relational databases: these are among the most common solutions for data storage.
- ETL (extract, transform, and load) systems: this is the process by which data is moved from databases and other sources into a single repository, like a data warehouse. Common ETL tools include Xplenty, Stitch, Alooma, and Talend.
- Data storage: not all data can or should be stored in the same way, especially when discussing big data. Knowing about this will tell you where and when you should be using a data lake or data warehouses.
- Automation and scripting: this is a necessary element of working with big data because organisations are able to collect so much information.
- Machine learning: while data scientists are usually more responsible for machine learning, it can be beneficial to have an idea of the basics.
- Big data tools: data engineers will often be tasked with managing big data, as well as regular data. This requires knowledge of and skill with big data tools and technologies like Hadoop, MongoDB, and Kafka.
- Cloud computing: organisations are increasingly trading physical servers for Cloud, so you will need to understand Cloud storage and computing.
- Data security: many data engineers are responsible for securely managing and storing data to prevent loss or theft.
A data engineerās education
A large number of organisations looking for a data engineer will be looking for a candidate with a Bachelorās degree. Some may also ask for candidates with Masterās degrees, or even PhDs. However, this will not always be the case. The only requirement likely to be universal is the need for a strong background in technology.
Degree subjects employers favour include computer science, mathematics, IT, and software engineering.
Certification
Employers may also ask for certification in one of several key areas:
- Amazon Web Services (AWS) Certified Data Analytics ā Specialty
- Cloudera Certified Associate (CCA) Spark and Hadoop Developer
- Cloudera Certified Professional (CCP): Data Engineer
- Data Science Council of America (DASCA) Associate Big Data Engineer
- Data Science Council of America (DASCA) Senior Big Data Engineer
- Google Professional Data Engineer
- IBM Certified Data Architect ā Big Data
- IBM Certified Data Engineer ā Big Data
- Microsoft Certified: Azure Data Engineer Associate
- SAS Certified Big Data Professional
Building a portfolio
Having a portfolio demonstrates what you can do for employers. You can add projects youāve completed independently or as part of coursework to a portfolio website (such as Squarespace). Alternatively, you can also add projects to the Projects space available on your LinkedIn account or to a website such as GitHub.
Getting an entry-level position
The other route into data engineering is to find an entry-level position as a business intelligence analyst or database administrator. You will then find opportunities to advance, to pick up new skills, and to qualify for more senior positions as you work.
Move into a career in data engineering
Have you been considering a career change into a new field of interest? Are you looking to move up the corporate ladder into management and senior roles in data engineering? If so, Oakleaf Technology, Change, and Transformation is ready to assist. Send us your CV today and one of our specialists will provide the advice and support you need.
With our help, you will soon be matched with the Contract or Permanent position in data engineering that meets all your personal and professional criteria.