What is a data engineer?

With the onset of the pandemic, there has been a steady growth of global digitalization, and with it came a huge surge of raw data that businesses can leverage for their growth.

However, businesses need a professional who can source this raw data from a reliable source, process it, and make it ready for analysis.

And this professional is no other than a data engineer.

Data engineering is one of the fastest-growing positions in the tech industry with a projected annual growth of 50% in terms of job opportunities, but there is an unmatched supply of talent.

Meaning, that there is a huge demand for skilled data engineers in the current job market and if you are an aspiring data engineer, now is a great time to grab these opportunities.

Read on to get an insight into the data engineer job description and related FAQs like the following:

What is Data Engineering?


Just like how regular engineers design and build things, data engineers set up pipelines that convert raw data into a readable format for data scientists and analysts to work with.

And this process of setting up pipelines and the conversion of data is known as data engineering.

Data engineering is far from being limited to the tech industry alone as its application is crucial in almost every sector.

Data engineers process and channel the data to facilitate machine learning and deep learning.

Also read: How to write a stellar data engineer resume in 2022?

Data Engineer Job Description


If you are considering a career in data engineering, you must be clear about the data engineer responsibilities to help you arm yourself with the required skills.

Given below are some of the general data engineer roles and responsibilities:

Data Architecture


Data engineers are responsible for designing frameworks and structures for data architecture to streamline the process of sourcing, processing, and maintaining data with respect to business requirements.

They must also test these structures to ensure that they fit the purpose.

Data Collection


To initiate the process of data engineering, data engineers must source data from credible sources and compile them for further extraction.

They need to identify opportunities for data acquisition, combine raw data from various sources, and enhance the data’s quality and reliability.

Developing Algorithms


The job description of a data engineer entails developing algorithms to transform the sourced data into interpretable patterns and actionable information.

The algorithms and prototypes must be developed while keeping the company objectives and security policies in mind.

Creating Models


Data engineers are also tasked with creating models that automate tasks by identifying patterns.

They develop prescriptive models that allow users to benefit from the recommendations for various results.

By using analytics tools like Apache Spark, Knime, and Tableau, data engineers generate resourceful insights for various industries.

Also read: What are the differences between a data engineer and a data scientist?

Data Engineer Requirements


Since there is no specific data engineering degree, the minimum educational qualification required to become a data engineer is a bachelor’s degree in computer science, mathematics, or a related field.

Additionally, having relevant experience, proficiency in programming languages like Python, C++, SQL, Java, and familiarity with Apache Hadoop and Spark are some prerequisites for data engineers.

The following are some of the other requirements you must meet:

Data Engineer Skills

Data engineers need to possess an array of specialized skills to meet the requirements of the data engineer job description.

Besides technical skills, you must have exceptional communication, presentation, and collaborating skills.

Listed below are some of the other essential data engineer skills that you must have:

SQL Data Warehousing
Coding ETL Systems
Data Scripting Data Automation
Data Architecture Cloud Computing
Data Security Apache Hadoop-based Analytics
Machine Learning API Integration
Data Structure Data Pipeline Management
Data Modelling Framework Designing
Report Generation Documentation

Also read: What skills should you put in your resume in 2022?

Data Engineering Certifications


Being certified is not a mandatory requirement to become a data engineer.

However, technical expertise in data modeling, mining, and other segmentation techniques is crucial.

Thus, having extra certifications validates your skills and proves your ability to meet the requirements of a data engineer job description.

The following is a list of some in-demand certification courses that you can take up:

  • IBM Certified Solution Architect -- Data Warehouse V1
  • Data Science Council of America (DASCA) Associate Big Data Engineer
  • Google Professional Data Engineer
  • AWS Certified Data Analytics Specialty
  • Data Engineering with Google Cloud Professional Certificate
  • IBM Data Engineering Fundamentals
  • Data Processing and Analysis

Also read: What are the top interview questions for a data engineer?

Data Engineer Job Description Sample


When you prepare to apply for data engineer jobs, you must tailor your resume according to the job description provided in the listing to increase your chances of getting hired.

For your reference, given below is a data engineer job description sample that you will come across in listings:

Job Overview

We are looking for a skilled data engineer to join our team of data experts at ILB. As a data engineer, you will be responsible for growing and optimizing data flow and pipeline architecture. You will be coordinating with software developers, data analysts, and data scientists to facilitate business growth and support new products.

Data Engineer Responsibilities

  • Designing data solution frameworks using Hadoop tools and technologies along with Cloudera, Scala Programming, HDInsights, and Azure
  • Ingesting data from databases, streams, and files and process the same with Hadoop, SQL database, Spark, and IoT
  • Developing programs using Python and Scala to clean and process raw data
  • Designing software codes for various cases using Big Data technologies and Python
  • Designing and developing high-volume, multi-threaded event processing programs
  • Implementing scalable solutions for growing data volumes by leveraging cloud technologies like Kafka and Pyspark

Data Engineer Requirments

  • Proficiency in SQL, relational databases, and programming languages like Python and Java
  • Hands-on experience with optimizing big data pipelines and data sets
  • Working knowledge of stream processing and message queuing
  • Exceptional organizational, teamwork, and project management skills
  • 3+ years of experience as a data engineer or analyst
  • Bachelor’s degree in computer engineering preferred

Also read: How to apply for data science jobs in 2022?

Key Points from the Blog


  • Data engineering is one of the fastest-growing positions in the tech industry with a projected annual growth of 50% in terms of job opportunities.
  • Data engineers set up pipelines that convert raw data into a readable format for data scientists and analysts to work with.
  • They process and channel the data that facilitates machine learning and deep learning.
  • The data engineer job description includes designing frameworks & structures for data architecture along with data mining.
  • By using analytics tools like Apache Spark, Knime, and Tableau, data engineers generate resourceful insights for various industries.
  • Besides technical skills, you must have exceptional communication, presentation, and collaborating skills to become a data engineer.
  • Technical expertise in data modeling, mining, and other segmentation techniques is crucial.

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