Data Engineer: Role, Responsibilities, Skills, Salary And More


July 9, 2021

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes.

Source: statista.com

Big data is transforming the way business can be optimized. Leveraging data can empower organizations with innumerable benefits, provided the data is of good quality and competent to perform complicated tasks. Over the passage of time, the management of data has become more significant.

It is, indeed, a technological art and science, to extract such huge volumes of data, validate it, manage it, and make the most of it for further use. That is where the role of a data engineer comes into the picture.

Let us understand what data engineering means and what roles does a data engineer play in maximizing the benefits of data.

What Is Data Engineering?

Data engineering is the science to collect and validate information so that it can be leveraged by data scientists. It focuses on creating systems to manage the collected information, in almost all major industry segments. It is a software engineering approach to design and develop different information systems.

Data engineering is designed to support the process of data management so that analysts, data scientists can utilize the data with security, accuracy, and swiftness. As the name suggests, data engineering looks at the engineering part – designing and building pipelines for data transformation and transportation so that when it reaches the data scientists, it is in a highly functional form. These pipelines are supposed to collect data from various sources and assemble them into a single data warehouse to showcase the data with uniformity.

And the resources that perform data engineering to their best are the data engineers!


Data Engineer – An Introduction

Data engineers are the human resources that are responsible for effective data engineering practices in any organization. They create data reservoirs and help in the management of those reservoirs by developing, testing, and maintaining databases and processing systems. They install pipelines that carry the sorted information that data scientists can extract for their further course of action.

Data engineers play an important role in understanding the objectives of any business and then aligning data with these objectives, by handling complicated databases and datasets. Based on this understanding, they create algorithms that can offer organizations access to necessary data, in a usable format.

What Does A Data Engineer Do

It is tricky to understand the difference between a data analyst, data scientist, and data engineer. It looks as if they all deal with data and perform the same jobs. But it is not true. Each of them has their own designated set of tasks to be executed.

A data engineer is involved in various activities, some of them are as below:

  • Make data accessible so that organizations can use it to better their performance
  • Collection and management of data, converting it into useful information
  • Build and maintain data pipelines and maintain databases
  • Collaboration with management to perceive organizational goals
  • Creation of new data validation processes and analytical tools
  • Design, build, test, and maintain data management systems

Why Become A Data Engineer?

There is so much data everywhere, that can be leveraged for better business opportunities across all workflows in the organization. Technologies that pertain to data handling are complex and need a certain skill to be managed in the best way possible. As the data gets more complicated, newer technologies come around to help get the exact value from the information heaps. That is where a data engineer can be of most help.

Pursuing a career in data engineering can prove worthwhile in terms of offering value addition to the organizational success, easy access to data, and great assistance to decision-makers in terms of offering them data in their desired format, at their desired time. Now that most companies have undergone a digital transformation and technologies like IoT and AI are taking over, the availability of heaps of data is quite evident and hence taking up the role of a data engineer is quite fruitful.

The world is moving towards BI and Big data. Data engineering is helping these technologies to connect increasingly with masses, offer well-governed data pipelines and extract the best possible output. Therefore, data engineers are in heavy demand and an increasing number of developers are trying hard to achieve all necessary skills to become a good one.

How To Become A Data Engineer?

Yearning to become a data engineer is one topic, but how to become one is what matters most. Here are certain key steps that must be taken to ensure that you become a successful data engineer:

  • Gain your undergraduate degree, preferably from universities, and start working on projects
  • Garner entry-level job experience
  • Gather professional certifications
  • Develop data engineering skills – coding, automation, database design, cloud computing, etc.
  • Continue getting a higher degree in engineering, computer science, etc.
  • Brush up your analysis and computer engineering skills
  • Keep posting your work on LinkedIn, GitHub, etc.
  • Involve in self-learning through online courses
  • Adapt project-based learning approach

Data Engineer Roles And Responsibilities

Generally, there are three major roles that are earmarked for data engineers:

Generalists – usually found in smaller teams in which data engineers are supposed to perform many data-centric jobs.

Database Centric – Found in larger teams in which data flow is a major activity and data engineers must have a higher focus on analyzing multiple databases with data warehouses.

Pipeline Centric – Found in a middle business segment where data engineers are supposed to work synchronously with data scientists to make the most of data.

Here are the multifaceted data engineer responsibilities that are expected to be performed by the task force:

  • Creation and maintenance of optimum data pipeline architecture for ingestion, processing of data
  • Assembling of huge, complicated datasets that adhere to business needs
  • Identification and implementation of internal procedural enhancements
  • Creation of necessary infrastructure for ETL jobs from a wide range of data sources
  • Work in sync with internal and external team members like data architects, data scientists, data analysts to handle all sorts of technical issues
  • Collecting data requirements, maintaining metadata about data
  • Data security and governance with modern-day security controls
  • Data storage with technologies like Hadoop, NoSQL, Amazon S3, etc.
  • Data processing with newer tools that help in data management from disparate sources
  • Finding hidden patterns from data chunks, creating models
  • Integration of data management processes into the organization’s current structure
  • Help in seamless third-party integration and develop a robust infrastructure
  • Conduct research and identify automation tasks
  • Learn and utilize different scripting languages

Data Engineer Skills

Data engineers must possess the necessary skills that can help them perform their best and make organizations leverage their best potential:

Technical Skills:
  • Fundamentals of distributed systems like Apache Hadoop, Apache Spark
  • Database systems (SQL, NoSQL)
  • C++
  • Data warehousing solutions, Amazon Web Services/Redshift
  • Data structures, data modeling, data lakes, data architecture
  • Azure
  • HDFS/Amazon S3
  • ETL tools
  • Machine Learning algorithms
  • Data APIs
  • Python, Scala, Java languages
  • Apache Airflow, Apache Kafka
  • ELK Stack
  • Operating systems like Solaris, UNIX, Linux, etc.
  • Business intelligence and analytics
  • Visualization/big data analytics/dashboards
  • Knowledge of working with connectors – REST, SOAP, FTP, HTTP, etc.
Soft Skills:
  • Communication skills
  • Presentation skills
  • Team skills
  • Collaboration

Data Engineer Salary Details

According to payscale, the average data engineer salary is $ 92,496 per annum. An entry-level Data Engineer with less than 1-year experience can expect to earn an average total compensation of $77,300. An early career Data Engineer with 1-4 years of experience earns an average total compensation of $87,822. A mid-career Data Engineer with 5-9 years of experience earns an average total compensation of $103,616. A senior data engineer salary, with 10-19 years of experience is around $117,902. In their late-career (20 years and higher), employees earn an average total compensation of $115,411.

According to indeed.com, the average salary for a data engineer is $128,607 per year in the United States and a $5,000 cash bonus per year.

According to glassdoor.com, the national average salary for a Data Engineer is $112,101 in the United States.

According to salary.com, the average data engineer salary in the United States is $108,473 as of June 28, 2021, but the salary range typically falls between $90,615 and $126,346.

Future Scope Of A Data Engineer

Data engineering is soaring high and there are newer trends that are coming up. Here is a peep into the possible futuristic trends that data engineers would enjoy, in their upcoming ventures:

  • There will be data engineering support for every team
  • Real-time infrastructure will become standardized
  • Data engineers will be involved in DevOps methodology
  • Product-based data engineering will rise further
  • Remote working for data engineers will increase
  • Increase in self-service analytics through modern-day tools

Data Engineer Interview Questions

Here are some of the frequently asked interview questions for the position of a data engineer:

  • What is data engineering?
  • What are the most essential skills for a data engineer to possess?
  • What data engineering platforms are you familiar with?
  • What programming languages and data modeling techniques are you comfortable with?
  • Which frameworks and applications are crucial for data engineers?
  • What are the essential qualities of a data engineer?
  • Why do you want a career in data engineering?
  • What are the differences between structured and unstructured data?
  • Can you elaborate on the daily accountabilities of a data engineer?
  • What is your experience with data modeling?
  • What is your experience with ETL and which ETL tools have you used?
  • Do you contemplate yourself database- or pipeline-centric?
  • As a data engineer, how would you get ready to develop a new product?
  • What data engineering platforms and software are you acquainted with?
  • How do you build reliable data pipelines?
  • Which computer languages can you use effortlessly?
  • What do you appreciate most about data engineering?
  • Tell us about your data engineering work experience.
  • What is a data-first mindset?
  • Which tools did you pick up for your projects and why?
  • Do you tend to focus on pipelines, databases, or both?
  • What is the major professional task you have overcome as a data engineer?
  • How Does a data warehouse differ from an operational database?
  • Do you have any familiarity with data modeling?
  • Can you distinguish between a data engineer and a data scientist?
Wrapping It Up

Data is omnipresent and forms the crux for any organization to succeed. BI and Big Data are the pioneering technologies that can offer the best of out these heaps of data. For the globe surrounded by data, business intelligence and analytics serve to be the front face of information in desired formats and layouts and data engineers are the ones playing their roles with complete efficacy.

It is these data engineers because of whom the raw data reaches the data scientists in its best, usable form. The future has a lot in store for data engineers and its associated trends!

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