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Data engineering

What Does a Data Engineer Do and How Can You Become One?

Data is so ubiquitous and valuable that it is touted as the new currency. From data analytics to data engineering, everything is data-centric. As Carly Fiorina, the former Chief Executive Officer of Hewlett Packard, said, “The goal is to turn data into information, and information into insight.” Data allows leaders to make informed decisions that will benefit their business, improve customer experience and achieve various organizational objectives. Therefore, it is pretty evident that data is essential to business. But when it comes to data and business, several questions come to mind:

  1. How do leaders look for data? 
  2. Where exactly do they go to unearth the relevant data?
  3. What makes them confident that the data they find fits their business objectives?

This is when not only data engineering or data engineers come into the equation, but data scientists and data analysts as well. Each of these roles is important in the data intake process. However, these roles are all very different.

Difference Between Data Engineers, Data Scientists, and Data Analysts

Who is a Data Engineer?

Data Engineers procure data from numerous resources and convertData engineering analyst the same followed by building and managing systems that generate this data. They transform and clean the procured data for data scientists and analysts to scrutinize. They make the data viable by writing complex queries. Their role is very similar to that of software engineers, mostly because they use the same guidelines like the ones used for software development, in order to build the architecture and data systems. Moreover, they have knowledge of algorithms and some of the important concepts in programming.

Who is a Data Scientist?

After the data has been received by the data engineer, the Data Scientist improves or optimizes it and shares it with the organization. Businesses need data scientists because they are the ones who come with in-depth knowledge of programming skills and statistical tools that can be used to extricate the data as well as provide analysis and insights that can aid in providing a fix to various business problems. They have the ability to transform data into actionable and beneficial data for the organization. Learn more about how to become a data scientist today!

Who is a Data Analyst?

A Data Analyst extricates data from the accumulated pool and uses various methods like data cleaning, conversion and modeling to comprehend this data. Their analysis and findings allows organizations to scrutinize various aspects of the business like its overall performance, market trends and needs and requirements of its clients that can be affected. Much like data scientists, the analysis provided by data analysts can help organizations make decisions that are heavily data-driven.

It may seem as though data scientists and data analysts are one and the same. The two roles certainly involve analyzing data. However, what sets one apart from the other is that data scientists create analyses and predictions for the future using data, and data analysts make use of the data to comprehend and make observations of the past.

This blog will primarily focus on data engineering. So, if you aspire to become a data engineer or would like to expand your knowledge about the role, you have come to the right place. We will help you explore the A-Z of data engineering

Related Content: Data Science vs Data Analytics – What’s the Difference?

Who Is a Data Engineer and What Do They Do?

First, we need to understand what data engineering is and what a data engineer does. 

Data helps organizations advance. It facilitates informed decision-making, therefore, it is essential. However, to look for and fetch the appropriate data, organizations require the assistance of the right people—enter the data engineer. 

How do data engineers work with raw data? 

A data engineer acquires all the raw data and builds and manages systems that can arrange the readable and viable data. Finally, they send this forward for further analysis by data scientists and data analysts. 

This system that weeds out all the raw data in a database is called the data pipeline. Next, the enormous pools in which unsorted data floats is known as a data lake. And lastly, the process of sorting out all the data is called data warehousing. 

The data engineer is responsible for numerous facets concerning the construction and operation of the data warehouse, which is the focal point of the data engineer. 

  • Data engineering teams own certified and superior quality areas in the data warehouse. 
  • They classify and arrange the metadata and characterize the warehouse’s data extraction process. 

These teams can progress and either be a part of or lead educational programs. In addition, they can share the competencies central to the team, which will consequently help other teams become better members of the data warehouse.

What does a typical day look like for a data engineer? 

The main focus of a data engineer is to metamorphose the raw data into something viable and readable before its presentation to an organization. Not just this, they are required to design, build, test, blend, manage, and optimize the data with the help of numerous sources. They make the infrastructure that will generate this data. The objective is to create data pipelines that flow smoothly. In addition to all this, they write complex queries to ensure that the data is easily accessible.

The typical day of a data engineer can look different depending on their workplace. 

What are the routine tasks data engineers do?Data analyst

  • Building, evaluating, managing, and maintaining the database
  • Transforming data into usable and viable data by building algorithms
  • Procure datasets that are in line with the requirements of the business
  • Work with the management of the organization to understand its objectives
  • Develop data analysis tools and new validation methods

For instance, if a data engineer works for a small organization, they will set up and operate the organization’s data infrastructure because there is a possibility that the framework is not formalized. This simply means placing and driving platforms like Hive, Hadoop, and HBase. Basically, the data engineer performs a range of general tasks related to data.

On the other hand, some data engineers are responsible for developing data pipelines in large organizations, or perhaps managing data warehouses. Also, it is relatively common for the data engineering and data infrastructure teams to come together and work on finding solutions to various problems. Both the teams are also required to automate some parts of the data engineering processes. 

Is data engineering important across sectors and which industries welcome data engineers?

Organizations depend on data to move ahead. This dependency will increase over time. Therefore, leaders require data engineers to provide the relevant data and make it usable to help them achieve their business goals and take it ahead. Data engineering is applicable across various industries.

Among all the sectors, the demand for data engineering has ballooned the most in Technology. The growth of data engineering has accelerated in the technology sector with a predicted 50% growth year-over-year in the number of open positions, according to a 2020 Dice Tech Job Report. Telecom and Financial Services are the other industries that require people in this role. Data engineers are also prevalent in Services, Marketing and Advertising, Computer Software, Retail, Banks, and other industries. 

Demand for the role of a data engineer

Data engineers occupy an important position in organizations and lately, there has been a tremendous demand for data engineering roles. As opposed to data itself, the demand for data engineers has escalated by five times. Let’s look at a few numbers to crunch this with:

  • According to the 2021 Data Science Interview Report by, in comparison to interviews for Data Science, Data Engineering interviews grew by 40% in 2020. 
  • The 2020 Emerging Jobs Report from LinkedIn included data engineering in the list of the top 15 emerging jobs of 2020. There is an estimated 33% year-on-year growth for data engineering jobs too. 
  • From 2017 to 2025, the growth rate in the demand for data engineers is estimated to rise from 18% to  31% every year. 
  • Based on some other reports, there was an increase of 88.3% in data engineer jobs in 2019. Presently, the data engineering services market is experiencing an 18% growth p.a, which is expected to hit 31% p.a by 2025.

Data is transforming the business, thus creating an urgency for leaders to hire data engineers to help them collect and manage enormous amounts of data. Data engineers, therefore, play a crucial role in the data consumption process. Compared to all new tech hires, the most sought after are data engineers. Data engineering seems like an enticing career option for those looking at making a shift in their careers due to its growing demand. This surge in demand may continue to grow because data dependency will be rampant.

What is a Data Engineer’s Salary?

When talking about the nitty-gritty of this role, the question about how much a data engineer makes comes up naturally. The basic average salary is said to be around $77,541. In the United States, the average salary that professionals in this field make is $110,748. However, the typical salary ranges between $92,671 and $129,215. According to Glassdoor, the average salary for a data engineer is around $142,000 per year. Of course, this salary range can differ depending on the person’s educational qualifications, skills, and experience. 

Academic qualification and required skills for data engineering

Many employers seek candidates with a computer science, information technology, or applied mathematics degree in their academic qualifications. Usually, data engineers have a software engineering degree. Some even have degrees in mathematics or statistics, which aids them because they can incorporate what they have studied to solve diverse issues.

For the skills, prior experience in developing big data warehouses that can perform extraction, transformation, and loading (ETL) on big data sets will come to your advantage. In addition, data engineers are also skilled with programming languages like Java, Python, SQL, and Scala. 

What skills to data engineers require?

  1. Programming Languages: Knowledge of various coding languages like Javascript, Python, and Scala.
  2. SQL Mastery: SQL is another language of data. With the help of techniques like correlated subqueries and window functions, a data engineer should be able to communicate the different types of complexities in SQL. Beyond this, a data engineer should be able to read and understand database execution plans. They should understand the steps, how indices work, the different join algorithms, and the distributed dimension within the program.
  3. Architectural Projections: A data engineer should understand a variety of things, including libraries, tools, resources, platforms, subtleties behind different characteristics of databases, computation, stream processors, properties, workflow orchestrators, message queues, serialization formats, and other similar technologies.
  4. Data Modeling Techniques: They must possess a thorough knowledge of normalization and denormalization tradeoffs, entity-relationship modeling, and dimensional modeling.
  5. ETL (Extract, Transform and Load): This data integration process allows data engineers to create a single data source by amalgamating data from numerous data sources. This single data source is then stacked in a data warehouse. Data engineers should know how to write systemized ETL that can adapt to evolution.
  6. Data Storage: As a data engineer, you should know how to store data. Therefore, when designing the data solutions for a company, you will have to confirm whether you should be using a data warehouse or a data lake.  
  7. Cloud Computing: Because organizations are progressively replacing physical servers with cloud services, knowing cloud computing and cloud storage is essential. 
  8. Big Data Tools: Data engineers may work and manage big data from time to time. Popular tools and technologies include Kafka, Hadoop, and MongoDB.

Interested in this course?

Which online course is best to learn data engineering?

Do you wish to become a successful data engineer? Various online data science courses can help achieve your goal. The MITxPRO Post Graduate Certificate in Data Engineering is one such online program that will help you accelerate your data engineering career.  

We hope this helped you explore the field of data engineering and added clarity to queries concerning the role of a data engineer. Now, let’s look at how you can pursue data engineering

The MITxPRO Post Graduate Certificate in Data Engineering will provide you with all the data engineering skills currently in demand. Additionally, the course will take you through some essential concepts, tools, techniques, and best practices that will help you learn all the data engineering essentials. 

What are the essential skills data engineers need?

– Building effective data architectures and warehouses 

– Designing data models

– Streamlining data processing 

– Automating data pipelines

– Data wrangling 

– Big data engineering

How does the MIT xPRO data engineering course help new learners?

Not only will you get the opportunity to receive personalized feedback and access live weekly office hours with course faculty, but also you will develop a GitHub portfolio to share with potential employers.

The course will help you solve big data problems and thrive in the data age.

Once you have completed this exciting MIT xPRO Data Engineering course, you will naturally have questions about job opportunities.

Here are some frequently asked questions about data engineering courses

1. Can data engineers work from home?

Yes, it’s possible to work from home as a data engineer. There are a lot of remote data engineering job listings that are available.

2. Why is data engineering in demand?

Data is crucial to all organizations, and they require professionals who can help them find the relevant data. That’s where data engineers come in. The demand is consistently growing.

3. What questions do interviewers frequently ask during data engineering job interviews?

  • What is data modeling?
  • Define data engineering in your own words
  • How does one distinguish a data warehouse from an operational database?
  • Do you have prior experience with data modeling?
  • What skills do you bring to the table?
  • Why do you think you are a good data engineer?

Learn more about the MITxPRO Post Graduate Certificate in Data Engineering course. Start your Data Engineering career planning today.  

By Annabel George

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