8 Top Skills for Data Engineers in 2021 and Beyond

As we are technologically developing, data engineering is being adapted into many segments of the market. Therefore, it is leading to creating more job opportunities. Many big and small companies are using data engineers to build their databases from scratch. 

Data Engineering is taking over many sectors including public and private. Non-IT industries like Healthcare, Real Estate, Defense, etc are adopting data engineering concepts due to technological advancements. Currently, hardly there are segments existing that have not already adopted the internet. However, sectors that are already using the internet to function, are using data in one form or another.

Now, the adoption of digital data on multiple platforms has enabled more working sectors for data engineers as well. Many data engineers are performing jobs like Cybersecurity experts, data administrators, data architects, etc. 

In this blog, I will throw some insight into a few skills you might find useful as a Data Engineer. Learning these skills will increase the exposure of job opportunities for you in the future. 

6 Popular Job Roles for Data Engineers

Before we dive into the discussion of data engineering skills, let’s know a little bit about a few of the job roles which data engineers perform in the industry.

1. Machine Learning (ML) Engineers

ML Engineers work on creating algorithms and databases for systems or AIs to learn. The data engineers pick a variety of structured and unstructured data and use it to create algorithms. Moreover, ML engineers also filter and modify codes to extract the final form of the code to use for various purposes. The ultimate goal is to improve the quality of the already existing raw data.

2. Technical Architect

Technical Architects are responsible for managing the technical or IT requirements of an organization. In other words, the main goal of a technical architect is to prepare, modify, or manage the overall structure of a program. 

Apart from the IT jobs, There might be multiple sectors like Healthcare, Defense, Real Estate, etc where a Technical Architect can be useful. Good knowledge of programming languages like SQL, SAP, Oracle, C#, etc, can offer you a good career as a Technical Architect.

3. Back End Engineers

Data Engineers can also contribute to the creation and management of the back end. As a back-end engineer, you can help in designing logic and execution of the back-end system. Moreover, the ultimate goal will be to contribute to achieving the targeted User Experience.

4. Front End Engineers

As the name suggests, front-end engineers contribute to the User Interface (UI). The job profile demands the creation of static websites and implementing elements like visual effects, icons, colors, animations, etc., on webpages.

Moreover, front-end engineers are supposed to make sure that the website created is compatible with multiple browsers and websites. 

5. Full-Stack Engineers

Full Stack Engineers are responsible for backend and frontend, both. These developers are getting popular in the market as they reduce the organization’s cost as well.

Moreover, Full-Stack developers also have comparatively better technical experience and knowledge of the field.

6. CyberSecurity Engineers

Well, the name gives it away anyways. The purpose of these engineers is to observe hardware, software, databases, and networks for the security of data. Moreover, security engineers make and implement security protocols in an already established network to enhance data protection. 

With data engineering skills, one can implement protocols in an AI system and automate the cybersecurity perspective. Many AI-based IT organizations have already started providing such kinds of services to businesses.

8 Best Data Engineering Skills to Give You an Advantage

Now, I will not waste more of your time with boring lectures and straight come to the point. Below, you will find a list of some of these skills along with the descriptions. Let’s begin then!

1. Machine Learning

Machine learning (ML) is a technique of teaching Artificial Intelligence (AI). In other words, data engineers use machine learning to teach systems techniques of data sorting and processing. 

If we have to give a few examples of machine learning, we can find plenty of them in the modern world. Machine learning has evolved technology and cybersecurity in such a way that many modern devices are now adopting this technology. 

A few real-life machine learning applications are:

i. Speech Recognition

Speech recognition enables devices in understanding the voice and reacting accordingly. In other words, speech recognition is using machine learning to transcript, voice commands, device control.

ii. Healthcare Applications

With tons of healthcare data fed to modern AI dedicated to the healthcare sector, our devices can predict many health issues before we even notice them. Machine learning has started saving lives for a few years. It can monitor healthcare data like Blood pressure, pulse, heartbeats, etc, and provide an almost accurate diagnosis. Due to the same, many users have got themselves tested and protected themselves against any possible health issue.

iii. Fraud Detection

Well, Machine learning is all about learning the data and patterns. Therefore, it is capable of detecting anomalies in the data trends. If we take the example of the banking sector, machine learning can detect abnormal transactions. Moreover, in such cases, it either alerts bank authorities for the same or puts a block on the account itself. These measures are taken by the AI to save bank customers from any possible fraud.

iv. Some other Machine Learning usages are:

• Data extraction is done through machine learning as well. The process is used to filter structured data and unstructured data;
• Data Scraping is used to keep only relevant data;
• Image recognition is possible due to Machine learning algorithms. Google Image search is the best example for the same;
• Cybersecurity threats can be avoided by detecting hardware, software, or network anomalies and analysis.

2. Programming Languages

Programming languages are the backbone of data engineering. There are a few programming languages that are required even to execute basic data engineering tasks. Further, I am mentioning a few programming languages which are popular among data engineers at the moment.

i. Python

Python is used to create an ETL framework under data engineering. The programming language is used due to its familiarity with Apache Airflow as well. Moreover, data engineers use Python to combine multiple blocks of Apache Airflow together. 

Python is used by data engineers to aggregate, reshape, and to join the data. Moreover, Python is also used to execute tasks like acquiring data from multiple APIs and restructuring them. Also, with frameworks like XGBoost, Scikit-Learn, etc, Python is helping in running machine learning jobs.

ii. R Programming Language

R is one of the most used programming languages among data engineers and data scientists currently. The programming language is used for data analysis and the development of statistical software. Moreover, the programming language is entirely free to use as it comes with a licensing of GNU GPL v2. However, learning Python is easier compared to R.

iii. SQL

Structured Query Language (SQL) is a programming language designed to access and manipulate databases as required. The language is capable of retrieving specific data from the database. Moreover, It can also be used to create, modify, or erase already existing data in the data warehouse. By combining SQL with Relational Database Management System (RDBMS) and HTML/CSS, data engineers can create and manage a website as well.

iv. Java

Java is an open-source and object-oriented programming language. The purpose of this language is to reduce the workload of developers. As a data engineer, you can write Java codes once and use them multiple times for multiple purposes. Moreover, Java can be executed on multiple platforms without having to recompile. However, the platform must support Java.

v. Scala

Scala is a static type programming language. The language is used to save complex applications from bugs and errors. Moreover, the JavaScript runtime of Scala lets you create big data and a huge ecosystem of libraries. The programming language is best known for its compatibility with the distributed data management system.

3. ETL Implementation in the Data Warehouse

ETL refers to the Extraction, Transformation, and Loading of data. The process is used to collect multiple types of data from different sources and put it into one data storage system. Moreover, the next step is analyzing the data collected. 

• The extraction process finds a data source and targets to extract the data to use it later;
• Transforming the data is important because data comes in many different forms;
• In the end, the Load is used to transfer the final data to the target location.

4. Presentation Skills

Well, a data engineering job is not only about playing with codes and building data structures. To execute any task properly, good presentation skills are a must for the data engineer. Data engineering is mainly a perspective-based job. Hence, data engineers might have to convince other team members or clients for the goal they want to achieve. Therefore, presentation skills might come in handy to help data engineers with the same.

5. Multiple Database Systems

Databases are divided into two categories; Relational and Nonrelational Database. Both databases are important and follow their procedures for data engineers to work with the data warehouses. Further, we will discuss both of these types in a little more detail.

i. Relational Databases

It refers to the database storing data in tabular forms. This structured database uses software like Microsoft Excel to store the data. Moreover, the data is easy to recognize and find due to their titles being given alongside. 

The other term used for Relational databases is Structured Query Language (SQL). The data format is used to manipulate the data, add new data, or even erase the required data. The best part of this data format is that it is very easy to manage. Also, bulk data can be stored and found in relational databases without much hassle.

A few relational databases are trending in the industry at the moment:

• Microsoft SQL Server

Microsoft initially released this relational database management system in 1989 as SQL Server 1.0. The database is used to store and manage data in the structural format for other applications and software to use. Microsoft SQL Server has multiple versions differentiated based on their technology, data size support, and the types of audiences.

• MySQL Server

MySQL is a relational database management system (RDBMS) written in C and C++ languages. MySQL is a free-to-use and open-source database. It can be used to store, modify, retrieve, and erase data with SQLs. Mainly, data engineers use MySQL with other software for an effective data management system.

ii. Non Relational Databases

Non-relational databases follow customized data storage structures according to the data. In other words, the data don’t have to be stored in tables, columns, etc. However, it allows multiple types of data to get stored without hassle. The non-relational databases can store big data without much trouble.

• MongoDB

MongoDB is a NoSQL database management system being used by millions of developers worldwide. The database stores data in JSON document formats. Therefore, storing and managing data becomes easier. The database management system provides many other features like indexing of the data, duplication and replication, load balancing, etc. Moreover, the MongoDB Atlas makes process and resource optimization easier.

6. Apache Hadoop

Apache Hadoop is used to store big data into a network of storage systems. It uses programming methods to store the data in an existing cluster of computers. Moreover, the purpose of Hadoop is to expand the storage capacity by connecting with multiple local systems instead of a single server. 

The Hadoop Distributed File System (HDFS) allows developers to write and test distributed systems quickly and efficiently. Moreover, it automates the data distribution system and uses CPU cores to store and utilize the same.

7. Apache Hive

The hive was initially developed by Facebook to process the structured data from Hadoop. The infrastructure was taken over by Apache and made open source. The purpose of Apache Hive is to implement protocols to simplify the data observation and querying process. 

The infrastructure stores schema in a database. However, it uses HDFS to store the processed data. Apart from that, for the querying process, Apache Hive uses SQL-type language called HiveQL.

8. Cloud Storage

Currently, many software and mobile applications are using cloud computing as the preferred data storage network. Therefore, the knowledge of cloud storage or cloud computing can increase the exposure of the career of a data engineer.

Apart from the storage part, the network of cloud computing is also used to execute tasks like networking, analytics, and intelligence over the internet. Most developers are preferring creating internet-based applications these days. The reason is that internet-based applications enable cross-platform scalability. Therefore, the requirement for cloud storage has increased. 

Key Takeaways for Data Engineers

After such a long discussion, I believe that summarization is important. Therefore, I will pick a few important points from the information I gave above. It will give you a short insight and in case, if you missed something important, you will be able to read it here.

• Data Engineering requires a good understanding of programming languages like Java, SQL, Python, and Scala;
• Some soft skills like good communication, good presentation, logical thinking, etc are important for a good career as a data engineer;
• Data engineers are helping blockchain engineers in executing blockchain business ideas seamlessly and with more security;
• Plenty of organizations are using relational and nonrelational databases. Therefore, having an understanding of both can rapidly grow the career;
• Some relational databases like MySQL, Microsoft SQL Server are popular these days in the data engineering segment;
• Non-relational databases are relying on frameworks such as MongoDB;
• Extract, Transformation, and Load Database (ETL) is one of the most important techniques used for converting the picked up data and finalize it to make it useful for multiple purposes;
• It is important to have the understanding and experience of Apache Hadoop and Apache Hive to grow a career as a data engineer;
• Data engineers are performing job roles like CyberSecurity experts, Data miners, data architects, data retrieval engineers, etc;
• One of the major roles data engineers are playing is teaching Artificial Intelligence (AI) by feeding it the data in multiple forms. Currently, AI is dependent on the data provided by data engineers. Accordingly, it learns and executes multiple tasks;
• Data Engineers are collaborating with multiple teams and people with different skill sets like Web designers, finance app developers, blockchain engineers, etc.

With time, applications of Data engineers will keep expanding in the market. Therefore, it is still a great opportunity to build a good career as a data engineer. These skills which you got to know about above can give you a great advantage if learned. 

As most of these skills can also provide you career opportunities in other fields apart from data engineering. Moreover, if you want to become an entrepreneur in the future, you might have a great advantage for that perspective as well.