Top Skills for a Data Engineer
It’s not surprising that Data Engineers are in high demand. Their skills are greatly needed as companies deal with a deluge of data sources. And let’s face it...it’s not easy to acquire those skills.
You’ve got to prioritize the most important ones. But be smart about it too. Some skills require massive training and expertise--but if you know the shortcuts, you can accomplish in a few minutes what might otherwise take months.
The following chart provides a sampling of some of the top skills and responsibilities described in LinkedIn Blog Posts, as well as the associated tools, and available resources for quickly ramping up on those skills.
...and, we’ve also included a few shortcuts that allow you to expedite some of the more resource-intensive processes:
Sources for Education
Build and maintain an enterprise data warehouse with cloud-based technologies
Snowflake, Amazon Redshift,
Google BigQuery, IBM Db2, Oracle Autonomous Data Warehouse
Cloud data storage
AWS, Azure, IBM Cloud
LinkedIn Learning--AWS Storage and Data Management
IBM -- Introduction to Storage
CBT Nuggets--Google Cloud Storage Online Training
Building and maintain critical ETL processes
Talend, SSIS, Panoply
Tableau, PowerBI, Looker, Domo
LinkedIn Learning--Data Visualization: Storytelling Coursera -- Data Visualization, by University of Illinois
SQL Server Database Modeler, Lucidchart
Expertise in SQL
SQL Server, OracleDB, MySQL, DbSchema, Visual Paradigm
Scripting languages for ETL
Python, Perl, Bash
ETL skills are a HOT commodity...
You may have picked up on a few of our not-so-subtle hints that ETL is a big deal for data warehouse engineers.
ETL is a complicated, lengthy and messy process, and a lot can go wrong. And even the most passionate Data Engineer may wish that they could close their eyes and ‘make it go away’, and focus on more fun parts of the job.
Want to learn about how you can create data pipelines and virtual warehouses without ETL? Promethium can SHOW YOU HOW.