The Most in Demand Data Engineering Skills (2023)

With companies looking to get more from their data, it’s no surprise that Data Engineers are so in demand.

In today’s fast-paced digital landscape and the speedy rise of AI, data has become the lifeblood of many businesses.  

As companies lean into the power of data to drive decision-making, the demand for skilled data engineers has skyrocketed – so much so, that it made it into LinkedIn’s list of jobs on the rise in 2023.  

So, whether you’re a seasoned Data Engineer or aspiring to break into the field, here are the most in-demand data engineering skills of 2023: 

Top 11 data engineering skills

1. Data Infrastructure

Solid knowledge of data infrastructure is the foundation of every data engineering role. Understanding data storage systems, data lakes, and databases is crucial for any data engineer for creating efficient and scalable data solutions. Expertise in setting up and maintaining data infrastructure using technologies like Hadoop, Apache Spark, and Amazon S3 is highly sought after by recruiters and hiring managers.  

2. Cloud Computing

As most businesses have migrated their operations to the cloud, proficiency in cloud computing has become an indispensable data engineering skill. 

Your big players in the industry are AWS, Azure, and Google Cloud, as they offer scalable, secure, and (somewhat!) cost effective solutions for data storage and processing.  

If you want to know more about which platform to learn, we’ve recently written about the industry shift in cloud computing.

3. Big Data Processing

Handling massive datasets requires specialized knowledge of big data processing technologies.  

Apache Hadoop, Apache Spark, and Apache Flink are essential tools for data engineers to process, clean, and transform vast amounts of information efficiently. 

4. Data Warehousing

Data warehousing is a critical aspect of data engineering – so much so that it can be a job by itself.  

If you’re new to it, it involves the design and management of repositories for structured data.  

Data engineers with expertise in data warehousing tools like Amazon Redshift, Google BigQuery, and Snowflake are invaluable in constructing data warehouses that facilitate seamless data analysis and reporting.

5. ETL (Extract, Transform, Load)

Data engineers are tasked with developing ETL pipelines that efficiently extract data from various sources, transform it into a suitable format, and load it into the target database or data warehouse.  

To main tools for this are often Apache NiFi and Talend, along with hands-on experience in data integration, which is highly attractive to employers. 

6. Data Modelling

Data modelling is a fundamental skill that allows data engineers to design the structure and relationships of databases or data warehouses.  

There are a number of data modelling tools out there that can help you. These include ER/Studio, erwin Data Modeler and DbSchema. 

7. Data Governance

Whilst it’s not so much a piece of tech to learn, we think an honourable mention needs to go to data governance. 

As data privacy and compliance regulations continue to evolve, data governance has emerged as a crucial aspect of data engineering. Data engineers must be well-versed in implementing data governance frameworks, ensuring data security, integrity, and compliance with relevant laws.

8. Machine Learning

While not the primary role of data engineers, familiarity with machine learning concepts and frameworks like TensorFlow and scikit-learn will definitely make you stand out from the crowd. 

Data engineers who can integrate machine learning pipelines into data processing workflows are highly sought after for their ability to build advanced data-driven applications. And in our experience, can command much higher salaries because of this… just saying 👀. 

9. Programming Languages (Python, Java, Scala)

Knowing your way around one of the core programming languages is an absolute must-have requirement for data engineers.  

Your main options are:  

• Python
• Java
• Scala 

If you’re struggling to pick, our recommendation is Python. With its extensive libraries and ease of use, it’s clear why remains a top choice for data engineering tasks.  

If you’re interested in learning more about programming language popularity, here are our top programming languages for 2023.

10. Creating and maintaining APIs

Traditionally, creating and maintaining APIs has often been something that sits more with a Software Engineer. However, we’re seeing a lot more hiring managers looking for Data Engineers with the experience/knowledge to do it. 

By mastering API development, Data Engineers can streamline the process of integrating data pipelines and services with other teams or external partners. This means you can create self-service data access for data scientists/analysts – reducing the dependency on manual data requests (which we all know is a massive bonus!). 

Whilst this is not a ‘must have’ skill, it’s definitely something that should be on your radar. 

11. Real-time Stream Processing 

Finally, we have real-time stream processing. 

With the increasing need for real-time data insights, we’re seeing a big rise in demand for experience in stream processing technologies.  

Apache Kafka and Apache Flink are popular tools for real-time data processing and analysis, allowing businesses to react swiftly to changing data patterns. 

If you’re a Data Engineer, what’s next? 

As you can see, data engineering has become a linchpin in today’s data-driven world, and the demand for hiring Data Engineers has never been higher.  

It’s no surprise that the average Data Engineer salary is creeping into 6 figures, with a Head of/Director of Engineering earning upwards of £170,000 in places like London. You can read more about Data Engineering salaries here. 

Whether you’re looking for your next step as a Data Engineer, or you just want some advice on the current market, why not give us a call?