Big Data Engineer Jobs

In today’s digital age, around 3.5 quintillion bytes of data are created every single day. This large amount of information, called big data, holds power to make predictions, show trends and revolutionise industries. However, collecting and transforming this data requires specialised expertise. 

Big data engineers are the professionals who create an infrastructure to store, process, and analyse this vast amount of data. Companies hire them to collect data from various sources and refine and store it for further analysis. 

In this data engineering guide, you will learn about roles, responsibilities, skills, and more about data engineers in detail so that you can understand what it takes to become a successful big data engineer.

Big data engineer

A big data engineer is a professional who specialises in designing, creating, deploying, and managing a company’s data architecture. They work closely with data scientists and analysts to transform raw data into usable formats for analysis. 

They handle large volumes of structured and unstructured data. They set up and maintain data pipelines that gather data from various sources, including databases, logs, and external services. They work with big data tools and frameworks to create high-performance data pipelines. 

All in all, they play a crucial role in managing and optimising data processes, making it easier for businesses to make data-driven decisions. 

Roles and responsibilities of a big data engineer

Big data engineers perform several tasks related to data. Some of these are: 

Collecting and transforming data: They are responsible for collecting data from reliable sources and converting it into a usable format. They do this by cleaning, aggregating, and structuring data using different techniques. Once data is cleaned, it is stored for analysis. 

Managing data storage: They also select and maintain storage solutions for a company’s data needs. They choose a database type based on the data’s nature and the company’s specific needs. They aim to store data in a way that improves its retrieval, scalability, and security. 

Assessing data quality: They check data quality in various ways to ensure it is accurate and complete. To achieve these goals, they may use data profiling to find inconsistencies in data. Data cleansing methods are used to remove inaccurate information. Validation rules may be used to reject data that does not meet predefined standards and goals. 

Ensuring data safety: They implement security measures to protect sensitive and confidential information. Regular audits and monitoring are also part of the data security role. These practices help them identify unusual activities that could lead to a security breach.

Optimising query: It is also the responsibility of big data engineers to write complex SQL queries in such a way that minimises the load on the database and increases the speed of the data extraction process. 

They analyse the execution plan of databases to understand how a query is processed. It helps them identify areas where the query needs improvement. 

Designing data architecture: They design system architecture according to their data type and needs. When designing the architecture, they consider various factors such as data volume, velocity, and variety. They ensure that the designed system is fast, robust, and flexible. 

Monitoring the performance: Data engineers ensure that data storage and processing tasks run smoothly. They regularly monitor metrics like query execution time, CPU usage, and memory consumption to check the system’s performance. When an issue is identified, they take suitable action immediately.

Skills and qualifications of a big data engineer

Big data engineers have a mix of technical and soft skills. The following are the essential skills you must have to become a big data engineer. 

Programming languages: They should be familiar with programming languages used for data engineering. Commonly used programming languages among big data engineers are Python, R, and Java. 

Database knowledge: You should have knowledge of different types of databases, both relational and NoSQL. Each database type is suitable for a different kind of data. Hence, you should know when and how to use these databases effectively. 

Data warehousing: You should have a deep understanding of different warehousing solutions, such as Amazon Redshift and Google BigQuery. You should know how to load data into a warehouse and optimise queries for performance. 

Data pipeline tools: You should know how to use tools such as Apache Kafka and Flume to securely move data from its source to destination. You should be able to construct data pipelines that connect different data sources, allowing your company to make informed decisions quickly.

Data modelling: You must be familiar with different data modelling techniques, such as entity-relationship diagrams and NoSQL schema design. You should also know how to apply data modelling principles to distributed and NoSQL databases. 

Data security: You should be familiar with techniques required to protect data from unauthorised access, theft, or breaches. Common examples of popular security techniques are encryption, authentication, authorisation, and data auditing. 

Cloud platforms: Familiarity with cloud platforms like AWS and Azure is essential for data engineers. They allow them to design, implement, and manage big data solutions. This knowledge will also help them improve their ability to resolve data processing problems effectively.

Problem-solving: Data engineers face various data-related challenges every day. They can face these challenges efficiently if they have problem-solving skills. These skills allow them to identify, analyse, and resolve complex issues on time. 

Communication: Data engineers need to share technical information and concepts with non-technical people. Effective communication skills allow them to convey complicated issues in an understandable manner. 

Steps to become a big data engineer 

Here is the step-by-step guide you need to become a successful big data engineer. 

  1. Complete your education: You first need a solid educational background in computer science, information technology, or a related field. 

A bachelor’s degree is one of the main educational requirements for data engineers, constituting 65% of the preferred qualifications. You can get it in a relevant subject such as computer science, electrical engineering, computer engineering, information technology (IT) and data science. 

However, getting a master’s or doctorate degree can increase your potential of getting hired. 

  1. Learn relevant skills: You should have a strong command over the basic technical and soft skills mentioned above. You should be proficient in programming languages, data management skills, big data tools, communication, and problem-solving skills. 
  2. Gain practical experience: Look for internships, collaborations, projects, and competitions to gain hands-on experience. It will allow you to apply your knowledge and skills in a professional setting. You will get a chance to sharpen your existing soft and hard skills and learn newer ones. 
  3. Build a portfolio: Create a portfolio to showcase your achievements. It is a tool you can use to demonstrate your capabilities to your potential employers and tell them how you can bring them profit with your skills. 
  4. Prepare your resume: Write a professional resume highlighting your qualifications, skills, and passion for the field. Make it clear, organised, and concise. Write a customized objective statement and skillset that suits the job description you are applying for.  
  5. Seek employment: Now that you have both education and skills, it is time to look for a relevant job. Start with entry-level jobs. Aim for higher-level jobs with experience. 
  6. Stay updated: The digital world is continuously evolving. Hence, if you want to excel in this field, you must keep yourself updated. 

You should join relevant conferences, meetings, and workshops to meet with like-minded people. It will allow you to learn from industry experts and discover how the industry evolves with time. 

Big data engineer salary

In the US, a big data engineer is expected to earn approximately $138,909 per year. It consists of an average yearly salary of around $113,750 and an estimated additional income of roughly $25,159 annually. 

Your salary can vary depending on factors such as your education, experience, and geographical location.

Is big data engineering a promising career? 

Data engineering is one of the most rapidly growing tech fields in the world. A 2020 report shows that the role of data engineers witnessed remarkable growth, with a 50% year-over-year increase. This statistic indicates that, undoubtedly, it is a promising and rewarding career choice for people interested in technology.

Do big data engineers code?

Yes, coding is an essential skill for big data engineers as they need it to perform various tasks such as data manipulation, pipeline construction, and query optimisation. You can learn this technical skill through various courses, both free and paid, available on websites like edX and Coursera. 


Big data engineers help companies make full use of the power of data. They design data pipelines, find data solutions, and ensure data quality to take businesses to new heights. 

Whether you want to join this career or are simply curious about it, know that it is an ever-evolving field that provides many career growth opportunities. It has a high demand, which is expected to increase further in the coming years. 

So, learn the required skills and acquire relevant knowledge. Gain practical experience and build a strong portfolio. You will soon be a part of this booming industry, contributing to the digital transformation of businesses across the globe.