In a data-driven world, behind-the-scenes heroes like data engineers play a crucial role in ensuring smooth data flow. Imagine being an online shopper who suddenly receives irrelevant recommendations. A data engineer investigates the issue, identifies a glitch in the e-commerce platform’s data funnel, and swiftly implements seamless data pipelines. While data scientists and analysts receive attention, it is the relentless work of data engineers that ensures accessible and well-prepared data for informed decision-making within organizations. What exactly does a data engineer do? How do they contribute to business success? Let’s dive into their world, explore the data engineer job description, roles, responsibilities, and answer your burning questions.

What is a Data Engineer?

Data engineers play a critical role in gathering, managing, and transforming raw data into valuable insights for business analysts and data scientists. Their main objective is to ensure data accessibility, enabling companies to optimize performance and make informed decisions. They design algorithms, analyze statistics, and align data systems with business objectives to maximize efficiency. Data engineers need strong analytical skills, the ability to integrate data from diverse sources, proficiency in programming languages, and knowledge of machine learning techniques. The data engineer job description is expansive, encompassing a wide range of roles and responsibilities that contribute to the data-driven success of organizations.

Sample Data Engineer JD

Data Engineer Roles and Responsibilities

The data engineer roles and responsibilities may differ from one company to another based on their requirements. However, some common data engineer responsibilities include the following: 

• Develop and maintain a perfect data pipeline design. 
• Improve internal processes by identifying, planning, and putting them into practice. Examples include automating manual operations, improving data delivery, re-designing infrastructure for increased scalability, etc.
• Create the infrastructure essential for effective data extraction, conversion, and loading from a wide range of data sources utilizing SQL and AWS big data technologies.
• Create massive, complicated data sets that satisfy both functional and non-functional business objectives.
• Construct analytics solutions that utilize the data funnel to offer actionable insights into gaining new customers, improving operational effectiveness, and other crucial company performance indicators.
• Assist stakeholders, such as the Executive, Product, Data, and Design teams, with technical data-related challenges and provide for their data infrastructure requirements.
• Maintain the privacy and security of data across international borders by using several data centers and AWS regions.
• Develop data tools for the analytics and data scientist team members to help them develop and optimize our product into a cutting-edge market leader.
• Work with data and analytics professionals to improve the operation of our data systems.

Also Read: Job Comparison – Data Scientist vs Data Engineer vs Statistician

Skills Required to Become a Data Engineer

If you aspire to become a data engineer, you must be acquainted with certain technical and soft skills. 

Technical Skills

To excel in their role, data engineers should possess the following technical skills:

Coding

Proficiency in programming languages like Python, Java, SQL, NoSQL, Ruby, Perl, MatLab, R, SAS, C and C++, Scala, and Golang is highly preferred by most companies. A solid foundation in coding is essential for data engineering positions.

Understanding of Operating Systems

Data engineers should have a deep understanding of operating systems such as Microsoft Windows, Solaris, Linux, Apple macOS, and UNIX. Each operating system offers unique benefits and caters to specific requirements. Familiarity with different systems can contribute to success in the field.

Data Warehousing

Data engineers are responsible for analyzing and storing large volumes of data. Therefore, they must be well-versed in data warehousing solutions like Redshift or Panoply. Experience in analyzing and managing data using data warehousing solutions is highly valued.

Data Analysis

In-depth knowledge of analytics software, particularly Apache Hadoop-based tools like MapReduce, Pig, Hive, and HBase, is desirable for data engineers. Proficiency in data analysis techniques and tools enables effective data processing and manipulation.

Database Systems

A strong understanding of database management is crucial for data engineers. SQL (Structured Query Language) is the most widely used database solution, so proficiency in SQL is essential. However, knowledge of other database solutions like Cassandra or Bigtable can benefit freelance data engineers.

Basic Knowledge of Machine Learning

While machine learning is the domain of data scientists, data engineers should have a foundational understanding of utilizing different data types in machine learning applications. Building knowledge in statistical analysis and data modeling can make data engineers valuable assets within organizations and set them apart in their field.

Soft Skills

To excel as a data engineer, it’s important to develop strong, soft skills in addition to technical expertise:

Communication Skills

Effective communication is essential as data engineers collaborate with colleagues from diverse backgrounds, including those without technical expertise. Being able to convey ideas and findings is crucial, especially when working with data architects and data scientists. Strong communication skills are also valuable in remote work environments.

Critical Thinking Skills

Data engineers need to think critically to identify and solve complex problems. Analyzing situations, thinking creatively, and developing innovative solutions is vital. Critical thinking is also applied in troubleshooting and designing data systems to address challenges effectively.

Problem-Solving

Employers value candidates who can tackle issues efficiently and proactively. Data engineers should be able to draw on their industry knowledge to address immediate problems and conduct thorough research or collaborate with colleagues to find long-term, scalable solutions. Strong problem-solving abilities are highly regarded in this role.

Check out – Cheatsheet for Data Engineers

Why Pursue a Career in Data Engineering?

The data engineering profession could be both lucrative and demanding. You will serve a significant part in a company’s success, offering easier access to data that decision-makers, data scientists, and analysts need to perform their jobs. Your problem-solving and programming skills will help you develop scalable solutions. 

Data engineers will stay in demand until there is a need for data processing, which, needless to say, will remain forever. According to Dice Insights 2019 report, data engineering topped the list of trending jobs in the technology sector, followed by computer scientists, web designers, and database architects. 

How Data Engineers Bring Value to Organizations?

Including the database, data engineers extract and obtain data from multiple sources. Such sources can include Oracle Database, SQL Server, Excel, MySQL, or any other data processing or storing software. Thereafter, they employ algorithms on this data to make it functional so that it can help various departments like sales, marketing, finance, and others to increase productivity. 

Data engineers are liable for managing an organization’s analytics. They equip your data with speed. Often, it’s difficult for businesses to make real-time decisions and precisely estimate metrics such as customer retention, churn, and fraud. For example, data engineers can assist an e-commerce firm to discover which of their products will gain higher demand in the future. Likewise, it will help them to target different buyer personas and render more tailored experiences to their consumers. 

Since the world is inclining more toward big data, data engineer certification can drive and use it to create more precise projections. Data engineers can enhance data models and machine learning by offering well-governed data funnels. 

Data Engineer Salary

According to Glassdoor, a data engineer’s average base salary in a year in the United States is around USD 97,848. However, the figure can vary based on various factors, such as the years of experience, the location of the company, and the organization’s size. 

In India, the average base pay of a data engineer is ₹8,75,000 per year. Again, the salary may increase on decrease based on the factors described above. 

For example, in metropolitan cities like Mumbai, Bangalore, and Delhi, a fresher in the data engineering profession can make an average base salary of around ₹7,00,000 – ₹9,00,000 annually. However, candidates with 4 to 6 years of experience can earn between ₹12,00,000 – ₹16,00,000 annually. 

On the contrary, professionals in smaller cities and towns will get a lower average base salary. 

Data Engineer Career Path

Similar to any profession, the positions and roles of data engineers also progress. In fact, various data engineers begin their careers as business intelligence analysts or software engineers. However, over time and with experience, they secure managerial roles or grow into a solutions architect, data architect, or machine learning engineer. Accordingly, data engineer job descriptions also become diverse, than just extracting, processing, and analyzing data. 

How to Become a Data Engineer?

Now that you are acquainted with the data engineer job description, let’s find out how to become a data engineer. Follow the steps below:

Step 1: Polish Your Data Engineering Skills

The first thing you need to do is to develop data engineering skills that include coding, cloud computing, database design, and coding skills. 

In coding, you must know JAVA, Python, SQL, NoSQL, Scala, and R. Moreover, you should be well-versed with relational and non-relational databases and must know how they work. 

Step 2: Acquire Certifications

An excellent way to improve your knowledge and abilities is to study for a certification test. This will allow prospective employers to verify your level of experience. The possibilities for certification include Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, and Google Cloud Certified Professional Data Engineer.

Check for data engineer job descriptions of different companies, you will know what they actually want. 

Step 3: Create a Portfolio of Data Engineering Projects 

Not only in data engineering but in most jobs, including Graphic Designing, Content Development, Website Designing, etc., a portfolio is a must in a job search to showcase your projects and work you have done previously. 

Using a platform like Wix or Squarespace, you may upload data engineering projects you’ve accomplished on your own or as a requirement for coursework to a portfolio website. A free alternative to a standalone portfolio website is to upload your work to the Projects area of your LinkedIn profile or a website like GitHub.

Step 4: Begin with an Entry-Level Role

Many data engineers begin their careers in entry-level positions like database administrator or business intelligence analyst. You can learn new talents and become qualified for more specialized professions as you acquire experience. 

Conclusion 

From the above discussion, one thing is sure- the world, especially of that large-scale businesses couldn’t operate seamlessly without data engineers. They are the heroes working behind the sets of big databases to ensure the proper flow of data through the pipeline. They ensure data is accessible, organized, and prepared to fuel thoughtful decision-making within companies. Moreover, they develop and analyze architectures that enable data extraction and conversion for prescriptive and predictive modeling.

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