Data engineering is overtaking “data science” as the hot skillset of the 2020s. Companies are actively seeking people to collect data and load it into pipelines for the rest of the data science team to clean and organize. Without this data, there would be no data science team – and more importantly, no data to gather important insights from. As we look to the year ahead, we scoured over 18,000 data engineering jobs to find what companies are looking for. These data engineering platforms and skills are good to learn for anyone looking for a job in data engineering, or for anyone already practicing who’s looking to round out their skillset.
Top Data Engineering Skills
Independent of the platforms being used, these are a number of specific data engineering skills that you should know. Our chart below lists the top 20 and my number of mentions.
Workflows & Pipelines:
As our chart shows, a big part of being a data engineer means being able to handle and create workflows. This includes hard skills like being able to manage a data warehouse, to team-based skills like DevOps and Agile practices. Being a team player and knowing how to adhere to a flow is imperative. There are a number of core data engineering skills that you need to know. Just as a writer needs to know basic sentence structure, data engineers need these as a foundation.
- Data Infrastructure: This means knowing the basic structure of data and how to use it, such as organizing, processing, retrieving, and storing data.
- Data Analytics: There’s always a need for someone to be able to do basic analytics, though, for a data engineer, this more so means formatting data so a data scientist or data analyst can work with it.
- ETL: Aka Extract, Transform, and Load, ETL means taking the data from its original source and converting it into something usable for your organization.
- Data Pipelines: A set of data processing elements connected in series, where the output of one element is the input of the next one.
- Computer Science: Often a foundational skill for many data professionals, computer science is helpful for knowing the basic structure of algorithms, math, and computation.
Cloud Engineering
This one was a bit surprising to us. Usually, Cloud Engineering involves its own job, but now data engineers need to know a healthy amount of cloud engineering as well. With so much data and so many workflows being cloud-based now, it makes sense to be able to handle the flow both locally and on the cloud.
Programming
Programming is one of the most important things for any data engineer, as you’ll be using a language (or languages) for everything from ETL to pipelines. Python and SQL lead by a fair margin, but Java and Scala prove to be in-demand as well.
Big Data
While many data engineers will be working with smaller datasets, with so much data being created daily, knowing how to work with Big Data will be commonplace.
Top Data Engineering Frameworks and Services
In addition to all of the data engineering skills listed above, here are a number of data engineering frameworks that companies are looking for. As you’ll see, many companies are using open-source platforms both locally and on the cloud. Many are also using proprietary services and platforms so a mix is the norm as our chart below shows.
Cloud Services:
Cloud-based services are the norm in 2022, this leads to a few service providers becoming increasingly popular. AWS Cloud, Azure Cloud, and Google Cloud are all compatible with many other frameworks and languages, making them necessary for any data engineering skillset.
Apache Spark
Coming in as the second most in-demand platform, Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters. It’s usable with multiple programming languages, is used by thousands of companies, and works with countless other frameworks, such as scikit-learn, pandas, TensorFlow, and more.
In turn, Databricks is a Unified Analytics Platform on top of Apache Spark that accelerates innovation by unifying data science, engineering, and business. The two together are very attractive data engineering platforms to know.
Workflows for MLOps
MLOps are in-demand across the entire data science ecosystem. MLOps helps address the key challenge of utilizing machine learning models in a production environment: how to continuously train, integrate, deploy, and monitor models. A few platforms, such as Airflow, Docker, and Kubernetes, are often part of any good MLOps workflow.
Data Streaming Services
No, not Netflix video streaming. Data streaming is data that’s continuously generated, rather than a static dataset that requires manual updating. Useful for gathering real-time insights, using data streaming services like AWS Kinesis and Apache Kafka will help you get the most up-to-date and scalable data possible.
Data Warehouses
A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data goes in, and people across the organization can take the data as they need it.
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. This makes it a very attractive data engineering platform.
Climbing the ranks is Snowflake, largely thanks to its intuitive and scalable nature. It works well for data of any size. It’s also cloud-based and works well with AWS and other cloud services. Other popular platforms include Hive, Amazon Redshift, and BigQuery.
NoSQL Databases
NoSQL databases provide a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. These databases are useful for big data and real-time web applications. Some popular platforms include the open-source MongoDB and Cassandra.
