What Are the Required Skills to Thrive as a Data Engineer?

It’s simple to overlook the volume of data that your smartphone, Zoom calls, and even your Wi-Fi-enabled dishwasher produce each day. 200 Zettabytes of data are expected to have been produced and stored globally by the year 2025. While merely keeping this data can be challenging, getting value from it is far more challenging.

Between 2020 and 2022, the total enterprise data volume will expand from around one petabyte (PB) to 2.02 petabytes. This has increased by an average of 42.2% yearly during the last two years. This post covers all of the fundamentals of data engineering, such as common roles and required skills to be successful.

What Is Data Engineering?

When it comes to enhancing the value of data, there are a number of internal and external elements to take into account.

Your company most likely produces data from internal products or systems, interacts with third-party vendors and applications, and is required to transmit data in a specific format for a variety of internal and external clients and use cases.

In accordance with compliance requirements like SOC2 or Personally Identifiable Information (PII), which you are required by law to secure, your business likely gathers and collects data. When this happens, the security of your data is prioritized, creating significant technical challenges for both data in transit and data at rest. Big data breaches are still making headlines, and if they happen to you, they might be disastrous for your business’s reputation and bottom line.

Your data must be not only reliable for your business needs, but also accessible to your end users and have integrity (accuracy and consistency). If your data is safe but useless, it cannot benefit your business. A data governance strategy has several components that call for specialized knowledge.

This is where data engineering is useful.

What Are the Roles of a Data Engineer?

A data engineer is like a swiss army knife in the world of data, performing a number of duties that often involve one or more of the basic elements of data engineering that were discussed before.

How data engineers do their tasks will depend on your company’s particular requirements.

A data engineer’s responsibilities include storing, manipulating, loading, aggregating, and validating data. This includes:

  • Building data pipelines and efficiently storing information for tools that need to be queried.
  • Checking the data for compliance with data governance standards.
  • Understanding the advantages and disadvantages of different data storage and query techniques.

For example, you might want to store and query data from many platforms when an organization employs Amazon Web Services (AWS) as its cloud service provider. Which approach is ideal will depend on a number of factors, including whether your data is unstructured or structured (or even semi-structured), normalized or denormalized, and whether you require data in a row or columnar format.

Isn’t your data key/value-based? Do complicated relationships between data exist? Should the data be altered or merged with data from other sources?

All of these decisions affect a data engineer’s capacity to use, curate, process, and store data.

How Data Engineers Increase Value?

In a phrase, data engineers increase value by automating and optimizing complex systems, turning data into usable and accessible business assets.

Required Skills to Thrive as a Data Engineer

It is not easy to explain this – but let’s put light on some of the basics which you can learn in best data engineering online courses.

Data storage and compute

While databases and data warehouses can store data in a variety of ways, file systems enable the storage of data in a wide range of file formats. Because each of these many formats is designed for a certain use case, data engineers need to be aware of the optimal tool for the task.

If you were keeping data on disc in a data lake, for example, there are a few common possibilities for data formats:

  • ORC
  • Avro
  • Parquet

These data formats are often powered by a metastore that keeps track of data locations and allows for data queries. Common examples include:

  • Apache Hive
  • AWS Glue Catalog
  • Databricks Delta Lake

Platforms that allow highly dispersed systems that use streaming can store data too. Some common examples:

  • RabbitMQ
  • AWS kinesis and AWS kinesis Firehose
  • Apache Kafka

In order to obtain the intended condition, data is usually processed after being saved. This might require compiling information from several sources, integrating it, performing aggregates on it, and finally storing the resulting item in a certain location. In data pipelines, a variety of compute alternatives are widely used, including:

  • Databricks
  • Apache Spark
  • AWS Glue

The output of data pipelines can be added back into a data lake, Using the meta stores and data formats stated above.


Mathematics is equally important when looking at different data structures to store data or algorithms to process data. It is crucial to comprehend how using particular algorithms in a specific dataset or storing data in a particular format affects performance.

Quality focused

No matter how efficient and well-optimized your data input and curation are, if the data is wrong, it won’t matter. A data engineer needs to be able to understand the intended result as well as the steps and tools needed for data validation.

A skilled data engineer can monitor, profile, and alert when data deviates from expected ranges and criteria.


This sums up the post. We hope that you walk away with a better understanding of data engineering, the role of a data engineer, and how they assist your business make better decisions. If you’re interested in data engineering and are looking for data engineering best courses, you can consider checking out Hero Vired’s data engineering program.

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