Data Engineers are in very high demand.
They design and build pipelines that transform and transport data into a format that can then be highly visible to others, such as a Data Analyst or Data Scientist.
This process requires a Data Engineer to take data from many sources and them into a single warehouse that represents the data uniformly as a single source of truth.
This requires a lot of data literacy, ‘data literacy’ meaning, they can read, write, and communicate data in context, including understanding of data sources and construction, analytical methods and techniques.
Many organisations are still trying to understand what role a Data Engineer plays, and like most things in technology, this is still evolving.
Why does Data Engineering appear to be so critical now?
Over last decade many companies have completed a digital transformation. This has resulted in unimaginable volumes of new types of data and a higher frequency.
Organisations need teams of data people who are solely focused on the processing of data in a way that allows them to extract value.
What experience and skills is required to be a Data Engineer?
Best to think of Data Engineer as a team of people with a range of data engineering skills. Which ones that are a priority will depend on many things, including the individual needs of a particular data function. Such skills can, and will, include:
- Software – Agile, DevOps, architecture, design, service orientated architecture
- Distributed Systems – this would include software engineer skills and software architect skills
- Open Frameworks – Apache Spark, Hadoop, perhaps Hive, MapReduce, Kafka and others
- SQL – this is a database staple and remains that way
- Programming – experience in certain languages such as Python
- Visualization/dashboard
- Cloud Platforms – AWS, Google Cloud, Azure
- Analytics – while mainly the realm of other data roles understanding some of the mathematical principles or probabilistic principles are necessary for being able to properly manipulate the data so that it is in a shape that is accessible for people who are doing the end analysis
- Data Modeling – knowledge is quite important now in the sense that a Data Engineer needs to know how they are going to structure tables, partitions, where to normalize and denormalize data in the warehouse etc. and how to think about retrieving certain attributes.
With all of this in mind, no wonder companies still struggle to understand exactly what they need when it comes to data engineering.
All we know is we talk to a lot of them, and they are great!
The Konnexus Team – specializing in the recruitment of Data Analytics, Business Intelligence, AI & Machine Learning.
