What is a Data Engineer & what can they do for your business?

So you’re looking to build a data team, but who should you hire? In the world of data there are numerous job roles, from Data Analyst and Data Engineer to Data Scientist. Before hiring a data professional, you need to know the difference between them. Take a look at our blog on defining data professionals to find out the difference

Once you know the difference, it’s important to understand the role and responsibilities and what they can do for your business! 

What does a Data Engineer do?

Data Engineers are responsible for setting up and maintaining data infrastructure. That is to say, they will build pipelines and manage the data integrity and security. They develop data processes for data acquisition, data transformation, and data modelling, typically using SQL, Python, Spark or Go. A Data Engineer solves problems with technology, setting up the foundation for the Analysts and Scientists to build on.

5 Data Engineer tasks 

  1. Ingest data – Data needs to be extracted from various sources into one place where it can be accessed, used and analysed. This transportation of data can happen in real time or ingested in batches. A Data Engineer will extract a variety of data types from the sources available.
  2. Build data pipelines – Data pipelines allow for data to move from one place to the next. As a result, data moves from disparate sources to a destination for storage and analysis. This may be a simple process or more complex, depending on the business requirements. An Engineer will build pipelines that can collect, manipulate, store, and analyse data.
  3. Store data – Data Engineers are responsible for the storage of data, both in the lake and in the warehouse. 
  4. Implement instrumentation – When analysing a business problem, a Data Analyst might discover that vital data, necessary to answering the question is missing. A Data Engineer would then implement instrumentation to provide the data.  
  5. Make data discoverable through meta data and efficient representation – A key aspect of the Data Engineer’s role is making sure other users can find the data they are looking for and have access to all the contextual information about the data. Metadata such as who created the data, when it was last updated, whether it contains Personally Identifiable Information (PII), when it should be deleted and where it comes from needs to be efficiently represented to ensure users can easily understand their data.

The alternative solution to hiring engineers

kleene’s SaaS software makes this easy, so you won’t need to make expensive data engineering hires. We provide scale-ups with the data infrastructure necessary to build their single source of truth and empower Analysts to deliver value (without the need for data engineering!).. Our end-to-end automation and analysis removes the headache of data. Don’t worry if there isn’t in-house data expertise, kleene can handle your strategy, report building and much more!

Book a demo today to start solving your data problem!