Data engineers are fundamental to the entire concept and discipline that is data. In fact, without data engineers, all other data practitioners would practically be jobless. After all, someone needs to build the infrastructure that actually makes data available in the first place.
As data becomes one of the main features of our modern, highly-digitised age, the demand for data practitioners of all kinds is rocketing. We live in a world dominated by supply and demand economics, so you’d have to assume that this supply deficit is driving the salary of data engineers up.
Firstly, let’s quickly delineate what it means to be a data engineer vs other data specialisms.
Data engineering involves knowledge of data logging, IoT sensors, ETL and ELT, data storage, distributed architecture, SQL and NoSQL, Big Data engineering and real-time processing. It overlaps with data science when it comes to cleaning data and loading it into dashboards or other applications.
“Data engineers are the plumbers building a data pipeline, while data scientists are the painters and storytellers, giving meaning to an otherwise static entity” – David Bianco from UrTheCast.
Let’s have a look at the salaries of various data engineers.
Table of Contents
Data Engineers: A Supply Deficit
Data engineers are currently at the centre of a global supply deficit. That is, there is simply not a sufficient supply of data engineers to meet global hiring demands. Even very small businesses are looking to hire data engineers – this demand isn’t led solely by global tech giants.
In the US alone, there are more than 600,000 IT jobs made available year on year, but only some 50,000 to 100,000 graduates to fill them. At current rates, some of those jobs will be vacant for nearly a decade! Data engineer demand in the UK and Europe is similarly very high, with jobs that contain the keyword ‘data engineer’ increasing massively year on year. Career Foundry found that data engineering is the 8th most sought after job in the US.
Below are the year-on-year growth figures for data engineers, according to four major sources:
| Source | When? | Year on Year Job growth |
| Hired State of Software Engineers Report | 2019 | 45% (38% Y on Y in 2017-2018) |
| Dice Hottest Tech Jobs 2020 | 2019 | 50% |
| LinkedIn’s Emerging Jobs Report 2020 | 2019 | 33% |
| Burning Glass Nova Platform | April 2018 to April 2019 | 101% |
As we can see, the year on year growth of data engineers is consistently high. As such, you’d expect salaries to also be high. This is broadly true – the demand for data engineers is driving salaries towards the upper percentiles.
Why Are Data Engineers In Demand?
Data engineering is a rising and fast-evolving field. Major changes in data technology date back only to the early to mid-2000s, including Big Data and cloud computing. Technologies such as ETL are just a few years old. Most of the big players in the data space are still very young – Google’s BigQuery and Amazon Redshift are just around 10 years old (as of 2022). Find out more about other tools that form part of a modern data stack here.
Whilst there were some data engineers prior to this new wave of technologies, who only needed to upskill rather than retrain, they were never going to meet modern demand.
Back in the early 2000s or 90s, most businesses wouldn’t need many data engineers and were working with on-premises now-legacy systems and RDBMS. Of course, SQL and RDBMS systems will never truly die, but the cloud has enabled the rapid deployment of complex data systems in both bigger and smaller businesses.
Data Engineers Require Niche Skills
You can’t blag your way into being a data engineer, nor can you fake it till you make it. Whilst anyone from a development or programming-oriented background will have some skills relevant to data engineering, data engineers require niche skills that are developing all the time.
Firstly, the challenges faced by data engineers are changing as businesses rotate between different technologies ranging from Hadoop to Spark, BigQuery to RedShift, and tons of semi-managed or automated enterprise-level platforms like Segment, Snowflake, mParticle, etc, as well as ETL services like Fivetran. The list is practically endless and new products are being developed all the time.
A data engineer will have to work with a business problem and engineer data services to solve it, most likely with the assistance of a data team. This requires an agile and up-to-date knowledge of the latest products on the market. Moreover, data engineers cannot forget about traditional programming skills solely because of the availability of managed or automated services – knowledge of Python is fundamental.
Now, machine learning and AI have somewhat leap-frogged Big Data as two of the primary keywords mentioned in cutting-edge data engineering jobs. As such, data engineers need to be familiar with technologies that are sometimes barely months old. This cutting-edge skillset makes the remit of data engineering quite a demanding one.
Data Engineering Job Ideas
Here is a short compilation of some of the common data engineering jobs you might encounter on a job search:
- Analytics engineer: Focussed on developing solutions for data analysts. Involves cleaning and transforming data and loading it into dashboards.
- Big data architect. Big data architects require skills in database architecture (e.g. BigQuery and Redshift), business intelligence (BI), data modelling and SQL.
- Business intelligence (BI) developer. Business intelligence developers will connect data using ETL to any number of business intelligence tools (Power BI, Tableau, SiSense, etc), cleaning and transforming the data in the process.
- Data warehouse developer. Focussed on warehousing, warehouse developers will need knowledge in cloud and relational database architecture, SQL, SQLServer, Oracle and ETL.
- Machine learning engineer. Key skills include machine learning, deep learning, natural language processing (NLP) and computer vision. Knowledge of ML in Python is essential here.
- Data Software engineer and solutions architects. Focussed on solving specific business problems, usually by scratch building solutions. Requires skills in various programming languages, such as Python, JavaScript, C#, etc, AWS, and distributed systems like Apache Zookeeper.
Data Engineer Salaries
Data engineer salaries are increasing. In 2020, Randstad found that in the US, the average salary of a data engineer registered 33% growth. Below are the average salaries for data engineers in the US and UK:
| Source | Data Engineer Salaries |
| Glassdoor 2020 and 2018 | $137,776 average (USA) £52,281 average (UK) |
| Indeed | £65,275 average (UK, London) |
| Reed | £58,798 average (UK) |
| Payscale.com 2020 | $91,694 average (USA) |
| Dice Salary Report 2020 | $113,249 average (USA) |
| Emsi Tech of the Future: 10 Emerging Jobs for 2020 | $76,500 to $221,500 (range) (USA) |
As we can see, the US averages are slightly higher than the UK averages after conversion from USD to GBP.
In fact, the UK doesn’t reach the top 5 highest paying countries for data engineering, which are:
- United States: $105,574
- Germany: $104,759 (or €92,355)
- Australia: $99,326 (or $139,077 AUD)
- Netherlands: $95,794 (or €84,514)
- France: $95,694 (or €84,425)
However, the upper range in the UK tends to be much higher in some cases, and data engineering salaries exceeding £200,000 are not uncommon. London shows the highest salaries for data engineering in the UK – even junior data engineers can expect around £50,000.
Conversely, if you look at the data engineering salaries offered by Google, Amazon, Microsoft and other tech giants, you find the following:
- Average data engineering salary at Amazon: $121,000
- Average data engineering salary at Apple: $166,000
- Average data engineering salary at Google: $126,000
- Average data engineering salary at Meta (Facebook): $166,000
- Average data engineering salary at Microsoft: $132,000
So, the upper limits may be lower in the US than elsewhere, even if the average is relatively high.
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Data Engineer Salaries Based on Experience
Here is some guidance to data engineering at different stages of experience. The spectrum from junior data engineer to senior engineer is continuous, so there are obviously plenty of stages in between each of these levels!
Entry-Level Data Engineer Salaries
At entry-level, a data engineer will need a degree or similar qualification in an IT-related field, as well as knowledge in databasing, SQL, Python or Java. ETL is also vital and any experience in Big Data architectures like Spark and Hadoop is a massive bonus. Knowledge of how to validate and clean data is also crucial. Data engineers do need some knowledge of analytics too, so being able to turn data into visuals via libraries such as MatplotLib, Ploty and Seaborn are potentially advantageous.
Average salary expectations for junior data engineers: £25,000 to £40,000 (UK), or $45,000 to $70,000 (US).
Mid-Level Data Engineer Salaries
Building upon pre-existing skills within the context of ML, AI and Big Data will progress an entry-level career.
Gaining vital practical skills in engineering projects will also increase salary potential. Qualifications at master’s level will also boost prospects, as will partaking in coding bootcamps. Also, you can read some of these top 10 books on data engineering.
Average salary expectations for mid-level data engineers: £40,000 to £65,000 (UK), or $70,000 to $100,000 (US).
Senior-Level Data Engineers
Senior-level data engineers take on the most complex projects and will need to demonstrate success across a portfolio of complex projects. Senior data engineering requires management skills too, as tasks will have to be delegated to others. A PhD may be useful here.
A complex and in-depth knowledge of cloud databasing architecture and Big Data is required. In-depth knowledge of Python, AI and machine learning. Complete knowledge of SQL is a must-have, as is in-depth knowledge of BI tools. It will also be necessary to stay up to date with industry developments and enterprise-level technologies.
Average salary expectations for senior data engineers: £80,000 to £200,000 (UK), or $120,000 to $200,000 (US).
Summary: Salary of Data Engineers: Complete Guide
Data engineers are still in demand today and this will likely continue for many, many years. The supply of data engineers doesn’t seem like it can possibly saturate the job market.
Anyone considering training to become a data engineer should be encouraged by these stats and figures. It’s not the easiest job in the world, but for those with a strong technical mind and appetite for problem-solving, it’s highly rewarding.
In summary, data engineering is a very in-demand job, and a job that pays well pretty much wherever you are in the world.
FAQ: Data Engineering Salaries
What are data engineers paid?
In the US, you’re talking around $100,000 per annum on average, with senior-level jobs ranging up to $250,000 or higher. In the UK, around £60,000 is a fair estimate, with an upper limit of around £200,000 or so per annum.
Do data engineers get paid more than analysts?
It’s impossible to say; it depends on the level of the role, company, task, etc. As a rule, they probably get paid similar salaries. Arguably, data engineering requires more specific industry-specific skills (e.g. programming), whereas analysts may be from a mathematics or statistics background.
Are data engineers in demand?
Very much so. Glassdoor found data engineers to be the 9th most in-demand job in the US in 2020. Virtually all sources indicate that there is a massive deficit of data engineers.
