Who is a Data Engineer and What Do They Do in Data Science?

Read it in 4 Mins

Last updated on
11th Mar, 2021
Published
22nd Apr, 2020
Views
3,330
Who is a Data Engineer and What Do They Do in Data Science?

With over 50 billion connected smart devices collecting, sharing, and analyzing data, it is undeniable that Data Science is ruling over the world and is here to stay. Not just this, big data is big money too. The industry is expected to generate over 274.3 billion U.S. dollars by 2022. Simply put, data science obtains large amounts of data from the internet and other smart devices and makes use of modern scientific methods, algorithms, processes, and systems to analyze this and predict customer behavior. This information is then leveraged in making major business decisions for growth and increased revenues.  

The incredibly large industry has multiple people performing different tasks, right from cleaning data, implementing predictive models, to creating comprehensible business strategies. While data scientists are the most sought-after in this industry, there are many other stakeholders involved in generating the data, such as data analysts, data architects, and data engineers. On average, data scientists are earning more than the typical software jobs, with an annual income of $1,13,000 annually. While the salaries of data architects and data engineers range from $103000 to $108000 respectively. 

Who is a data engineer? 

Data engineers are the foundation for the data science industry as they convert the raw data into a useful format for the data scientists. They also find the trends in data sets that are in turn used to convert raw data. While a data scientist is more concerned with the end-user, a data engineer is the one interacting in the back end to collect vast amounts of data. Typically, a data engineer is concerned with building pipelines that convert the data into formats that data scientists can use.  

Data Engineering Roles

The three major categories of a data engineer based on the company size and roles are as follows:  

1. Generalist

Typically, generalists are found in small teams or companies where their role is broad. They are the one-man-army for data and are responsible for every step of the data process, from streamlining data, managing it, to analyzing it from time to time. Since these companies do not have numerous users, the systems architecture knowledge required for this role is also less.  

2. Pipeline-centric 

Pipeline-centric data engineers are found in mid-sized companies and convert huge data into a useful format for analysis. They are usually required in companies with complex data needs and work closely with data scientists. A pipeline-centric data engineer is expected to have an in-depth knowledge of computer science and distributed systems.  

3. Database-centric 

Database-centric engineers are responsible for setting up and populating the analytics of databases. They go beyond creating pipelines and adjust the database into bite-sized formats for quicker analysis. They are concerned with ETL (extract, transform, load) work and creating table schemas, and are required in large companies with data distributed across databases.   

Data Engineer Responsibilities 

Some common responsibilities of a data engineer include: 

  • Developing and constructing architectures; testing and maintaining them 
  • Strategizing architecture to align it with business requirements, conducting relevant industry research and providing updates/solutions to business questions/stakeholders 
  • Data acquisition and dataset process development; utilizing this data to address business issues 
  • Identifying ways to improve the reliability of data, its efficiency, and quality 
  • Deploying advanced analytics programs, optimizing machine learning tools and statistical methods; identifying tasks that can be automated with the same 
  • Using different programming languages and tools 
  • Finding hidden patterns of customer behavior using data 

Data Engineering Skills

Data engineers are architects at heart working on large-scale systems or huge amounts of data. Technical knowledge of software such as Apache Hadoop, NoSQL, Apache Spark is highly in demand today. Expertise in setting up cloud clusters and machine learning is also highly beneficial for aspiring data engineers.  

Without data engineers, data scientists will not function, making them a critical-first member of the data science team. It was found that bad data is costing US businesses alone $600 billion annually, which shows the growing need for organized data – and data engineers are vital for this process. If data is something that excites you, a variety of online courses, right from boot camps to introductory practice modules are available to get you started.

Profile

KnowledgeHut

Author
KnowledgeHut is an outcome-focused global ed-tech company. We help organizations and professionals unlock excellence through skills development. We offer training solutions under the people and process, data science, full-stack development, cybersecurity, future technologies and digital transformation verticals.