Top 10 Skills for Data Engineers in 2021
Career InsightsFeatured Post2021Azuredata engineeringposted by ODSC Team January 11, 2021 ODSC Team
Data engineering is an increasingly sought after job role, and despite a tumultuous 2020, the chart above showed it’s more in demand than ever. Due to the pandemic, jobs were scarce in April but quickly rebounded before the traditional summer lull hit. Demand increased significantly in the final quarter of 2020.
With so many jobs available, we decided to find out which skills were most sought by employers when hiring data engineers. We looked at major data science job boards – mainly in the US and Europe – and discovered the top 10 skills for data engineers in 2021.
Python is a top skill for software engineers, machine learning engineers, and data scientists. Thus it is no surprise that over 61% of data engineering roles mentioned these skills. The Python language and its libraries are very suited to building pipelines and workflows for data engineering. It’s also the native language of major workflow management platforms such as Airflow and Kubeflow.
With more than half of job postings listing SQL as a skill at 56%, it’s an important skill for data engineering jobs in 2021. Aside from being a core data science language in general, SQL is especially useful from a business point of view, such as being able to model business logic and create reusable data structures.
Much of the software infrastructure has migrated to the cloud and the trend continues. Cloud experience was listed in 45% of the job descriptions we reviewed. AWS was the dominant platform followed by Azure. Many employers seem to treat cloud platform skills as interchangeable or at least expect expertise on one platform to translate to others.
4. Big Data
Big data is the norm for a lot of organizations now, so it should be no surprise that 43% of job listings ask for big data expertise. Whether it’s loads of banking information, huge customer databases, or looking through mass amounts of social media data, there’s a lot of data to work with and countless benefits to exploring it.
ETL – aka Extract, Transform, Load – came up in 40% of data engineering job postings. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. ETL also makes it possible for different types of data to work together.
With 37% of data engineer job listings asking for Spark knowledge, it’s a good skill to have. Considering data pipelines are a huge part of what makes a data engineer special, it makes sense that Spark – a framework built for data pipelines – comes up frequently.
At 32%, Java is not to be overlooked. As an older coding language, many businesses still have their existing processes built in Java, so it makes sense to keep using what’s already working well. Many data pipeline tools – such as Hadoop – are built using Java, and have become the standard for data engineering.
8. Machine learning
Given the clear and obvious growth of machine learning as a field, it shouldn’t be a surprise that machine learning expertise is still the most sought-after skill in data engineering at 26%. Popular and open-source frameworks, libraries, and tools make machine learning a realistic approach for many organizations to tackle AI, as opposed to more granular, expensive, or resource-intensive approaches like deep learning. Knowing hot topics in machine learning is a massive difference-maker.
At 24% of job listings, the Apache Hadoop framework is an ecosystem in itself, as it’s actually a collection of open-source tools. It allows for the distributed processing of large data sets across clusters of computers using simple programming models.
10. Data Science
The close cousin of data engineering, data science showed up on 23% of data engineering job postings. Data engineering lays the groundwork for data science by creating the data pipelines and getting it ready for machine learning algorithms to be built. While a data engineer might not be doing data science directly, they will likely be working with data scientists for larger projects.
Pulling it all together
That’s a lot to learn to become a data engineer in 2021 and there are lots of ways to go about learning everything above. With ODSC events and Ai+ Training, you can learn all of these core skills and become a data engineer in 2021 without worrying about a college degree.
ODSC East 2021:
Our flagship event, ODSC East 2021, is going virtual again this year from March 30th to April 1st. As the only data science virtual training conference, you’ll gain the skills you need for anything under the data science umbrella – including data engineering.
In the MLOps & Data Engineer focus area, you’ll learn the specialized skills you need to become a practicing data engineer, focusing on tangible, real-world skills that employers look for. Stay tuned for speaker announcements and talk titles.
Ai+ Training Platform:
On the Ai+ Training Platform, you gain access to countless on-demand training sessions that cover everything data engineering – including all of the topics above. Here are some standout sessions:
- Scaling Your ML Workloads From 0 to Millions of Users: Shashank Prasanna | Sr. Technical Evangelist, AI/ML | Amazon
- Streaming Decision Intelligence and Predictive Analytics with Spark 3: Scott Haines | Principal Software Engineer | Twilio
- Continuously Deployed Machine Learning: Mat Humber | Distinguished Faculty Member | General Assembly
- How to do Data Science with Missing Data: Matt Brems | Global Lead Data Science Instructor | General Assembly
- SQL for Data Science: Mona Khalil | Senior Data Scientist | Greenhouse
- Data Science in the Industry: Continuous Delivery for Machine Learning with Open-Source Tools: ThoughtWorks, Inc. Team
- Build an ML pipeline for BERT models with TensorFlow Extended – An end-to-end Tutorial: Hannes Hapke | Senior Machine Learning Engineer | SAP Concur
- Data Science and Machine Learning in the Cloud for Cloud Novices: Joy Payton | Supervisor, Data Eduction | Children’s Hospital of Philadelphia