Data engineering refers to the practice of designing and creating systems that collect, store and analyse data on a large scale. It is a diverse domain that can be applied to almost every industry. Enterprises today have multiple data sources and huge volumes of raw data. They need skilled professionals and technology that can process and analyse this data to make it usable. Data scientists and analysts are responsible for processing this data to derive valuable actionable insights that businesses can add to their strategies for business growth and scalability. Before the raw data reaches these data specialists, it must be usable so that they can make sense of it. This is where data engineers come in. 

Data engineers do much more than just simplify the work of data scientists and analysts. If you enter this domain, you play a significant role in adding value to a world that will produce 463 exabytes of data every single day by 2025. One exabyte has 18 zeros, in case you were wondering. Data engineers are vital to the functioning of rapidly growing domains like deep learning, machine learning and IoT. Without an engineer to channel their data, these domains will collapse. 

What is a Data Engineer?: An Overview

in short, a data engineer is responsible for the design, maintenance and optimisation of data infrastructure to help with data management, collection, accessibility and transformation. as a data engineer, you are responsible for creating the pipeline that converts raw data into a usable format so that consumers and data scientists can use it. the role of a data engineer has evolved today to take on core aspects of data within data science and software engineering. they use principles of software engineering to create algorithms that can automate data flow processes. data engineers also work closely with data analysts and data scientists to create infrastructure for machine learning and data analytics from end to end. 

Data engineers facilitate data access and structuring within organisations to provide the scalability and speed enterprises need for the delivery of analytics and insights from their data. As a data engineer, you work towards simplifying the lives of the data consumers and analysts and allowing them to drive greater impact. 

Generally, the structure or format in which data is stored is not optimal for analysing or reporting of that data. For instance, an application might be equipped to serve 10000 individual record requests concurrently. However, your data scientists will need access to crores of records at a time. In both situations, separate approaches will be required to solve problems. Data engineers play a critical role in bridging this gap.

As a data engineer, your primary responsibility is ensuring data is always accessible, secure and available for stakeholders to use or view whenever required. You will have a large number of responsibilities, which can be broken down into two key categories:

1. Data structuring and management:

A data engineer implements and maintains the architecture and infrastructure that underlies data storage, generation and processing. The responsibilities that come under this category are:

• You will build and maintain data infrastructure to optimise data extraction, loading and transformation from a large number of sources like GCP (Google Cloud Platform) and AWS (Amazon Web Services).

• You ensure constant data accessibility and implement the data policies of the organisation, particularly those related to data confidentiality and privacy. 

• You help improve the speed, performance and reliability of data systems.

• You create optimised data pipelines, warehouses and reporting systems that help in solving business problems.

2. Data analysis and insight generation:

As a data engineer, you play a fundamental role in creating platforms enabling a data consumer to analyse data and derive insights from it. The responsibilities in this category are:

• You help clean and wrangle data from primary and secondary data sources into usable formats that data scientists and stakeholders can easily access and understand. 

• You help develop APIs and data tools to enable data analysis.

• You deploy and monitor ML algorithms and statistical analysis methods within production environments. 

• You collaborate with data scientists, engineering teams and various stakeholders to help organisations to leverage data in a way that meets business objectives. 

Depending on the enterprise and industry you work in, the requirements and expectations from data scientists can vary. You can get a clearer picture of the exact requirements by looking at top companies such as Google and Netflix. This will help you understand which skills employers are looking for and which ones you should work on more. 

Skills Needed to Become a Data Engineer:

The field of data engineering is fundamental for bisecting data science and software engineering. There are no rigid steps you need to follow to be a data engineer. However, understanding a few significant actions can go a long way. Here are some key knowledge and experience areas you should know about before you start along the data engineering path. 

1. Understanding of NoSQL and SQL databases:

One fundamental skill data engineers must have is understanding the working of databases. They also need to know how to write queries that can retrieve and manipulate data. This gives you an edge and you don’t need to start training from scratch.

2. Knowledge of data processing tools and techniques:

This includes advanced tools like Apache Kafka, one of the most popular data processing tools today.

3. Knowledge of at least one programming language: 

Programming is an almost mandatory skill that data engineers need to have. Languages like Scala and Python are widely used among data engineers. You can find several programming courses that teach you from scratch or from whichever level you are at presently. 

4. Understand the working of distributed systems: 

There are several challenges that businesses face when they design large applications and data systems. Understanding how to work around them is a beneficial skill to have. 

5. Knowledge of cloud computing: 

With a growing number of companies depending on cloud services for their data infrastructure requirements, you can stand out as a data engineer by learning skills like designing and engineering data solutions via providers like AWS , GCP and MS Azure. You can take advantage of Koenig’s advanced training courses that will give you all the skills and knowledge you need. 

Why Choose a Data Engineering Career?

A career in the data engineering domain is both challenging and rewarding. You play a significant role in the success of your organisation, offering easier data access so that data analysts, data scientists and other key stakeholders can perform their jobs. As a data engineer, you depend on your problem-solving and programming skills to drive scalable solutions. 

Data engineer salary:

Data engineering is one of the best paying career options today in the IT industry. According to Glassdoor, you can earn an average salary of USD 111,933 per annum in the US with proper data engineering training. Some even earn as high as $164,000 per annum. Other roles in the data industry also earn very well but are nowhere close to what data engineers earn. For example, data analysts earn about $68,000 per annum, while data administrators earn an average annual salary of $81,444. 

As you can see, there are several benefits of earning a data engineer certification in your career. Give your career the boost it needs and enrol in a course today.

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