Data Engineer Skills for 2022

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8. DevOps.
A year ago, we recognized this key foundational piece for the Data Engineers knowledge as part of programming.
This year it is broken into its own multi-piece area. This area includes Software Development Life Cycle (SDLC) and Continuous Development (CD) and Continuous Integration (CI) techniques and tools like Jenkins , Git , and GitLab.
The process especially tied into DataOps and Data Governance results in higher data quality practices and better more accurate results.
7. SQL.
Can’t get away from those schemas and their infamous joining syntax yet!
In fact, more cloud-based systems are adding SQL like interfaces that allow the usage of SQL, for instance Google’s Looker or Amazon’s Athena and QuickSight combination.
Relational Database Management Systems (RDBMS) are key still to data discovery and reporting no matter where they reside.
6. NoSQL.
I keep hearing from organizations saying Hadoop is not important as we are moving to the cloud.
Let’s set the record straight here… Google BigTable, AWS S3 , Azure File and Blob are all related and manage hierarchical file data like the open-source ecosystems of Hadoop .
The cloud is full of unstructured or semi-structured (lacking a SQL schema) data stores, in fact over 225.
NoSQL , whether open-source Apache based, or MongoDB and Cassandra are all the rage in 2022.
Knowing how to manipulate key value pairs and object formats like JSON, Avro or Parquet is still a necessity for these.
 
5. Data Pipelines.
Desperate Data Lakes keep getting new names like DataBricks Lakehouse and Snowflakes Data Cloud implementations, same thing, new year. Operating with real-time streams, data warehouse queries, JSON, CSV, raw data is a daily occurrence.
The way and where data engineers set up storage may change data engineer skillsets and tools that are required for the ETL / ELT injection.
This is one area that is getting more complex and skewed depending on the source and resource used.
4. Hyper Automation.
Value added tasks, like running jobs, schedules, events, are now in a data engineer’s skillset requirement in 2022.
The last 10 years shows this trend getting more predominant with specialized Scripting and Data Pipelines  tasks required to successful move data to the cloud.
Gartner states that “the most successful hyper-automation teams focus on three key priorities: improving the quality of work, accelerating business processes, and increasing decision-making agility. “
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3. Visualization.
Exploratory Data Analysis (EDA) appears again now as part of Data Engineers talents to ensure ETL /ELT work mentioned earlier is successful.
Working with tools like SSRS, Excel, PowerBI , Tableau , Google Looker, Azure Synapse is a must.
Data quality of the resultant data is crucial as the Data Engineers processes and visualizes datasets.
2. Machine Learning and AI.
Last year we mentioned these subjects at the same position, and knowledge of terminology and familiarity with algorithms remain an important part of the Data Engineers skillset.
At minimum familiarity with Python’s libraries NumPy , SciPy, pandas , sci-kit learn and some actual experience with Notebooks (Jupyter or online cloud) is vital.
Taken to the next level in cloud-based tools like AWS Sagemaker, Microsoft’s HDInsight, or Google’s DataLab toolsets. This fields’ toolsets are getting more complex every year.
1. Multi-Cloud computing.
Still number one for a second year, but just add the word multi in front for good measure.
No longer content to be tied to single cloud vendors companies are opting to join the multi-cloud, instead of which cloud technology to choose, 76% of enterprises have already chosen a couple.
Cloud spending in 2022 will reach $482 billion.
A Data Engineer still needs to have a good understanding of the underlying technologies that make up cloud computing and in particular, knowledge around IaaS, PaaS, and SaaS implementations.