How to Become a Big Data Engineer?

A Big Data Engineer is one of the most talked-about job profiles today. Being a common term, this role enjoys great demand. A Big Data Engineer is undoubtedly a great option for all those inclined to start their careers in the field of Big Data. But, have you ever wondered how to bag this position? 

If yes, then look no further. This blog covers all the vital aspects of how to maneuver your way to become a successful Big Data Engineer.

Introduction to Big Data

Before understanding how to become a Big Data Engineer, let’s quickly understand the term ‘Big Data’ first. 

Back in the early 2000s, data generation was limited. But with the advent of various social media platforms and multinational companies across the globe, the generation of data has increased by leaps and bounds. According to the IDC, the total volume of global data is expected to reach 175 zettabytes in 2025. That’s indeed a great deal of data.

Below are a couple of statistics from Datafloq and Statista about Big Data and what the future has in store:


Not only is the volume of data increasing, but its velocity is also hitting an all-time high. Having said that, Big Data also refers to data in various formats.

Below are the different types of Big Data:


All this data is termed as Big Data. Big Data refers to massive amounts of data that cannot be stored, processed, and analyzed using traditional old school methods. The quantity is simply too large. 

To overcome this challenge of Big Data, various frameworks like Hadoop, Spark, Cassandra, and Apache Storm are used. 


Big Data Engineers work towards handling all of this Big Data with the help of these frameworks. Now, with that let’s move on and learn more about this job role and understand how to become a Big Data Engineer.

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Who Is a Big Data Engineer?

As mentioned earlier, data generation has increased all across the world. But, it is of no use until it is processed and analyzed competently. Big Data is analyzed to derive meaningful information from it, which in turn improves overall performance. By doing so, organizations can enhance their business decisions, products, and marketing effectiveness. And professionals in the field of Big Data aid this task. 

One of the best job roles in this field is that of a Big Data Engineer. Big Data Engineers are professionals who develop, maintain, test and evaluate a company’s Big Data infrastructure. They play with Big Data and use it for the organization’s benefit and growth. 

The roles of a Data Engineer and that of a Big Data Engineer are interchangeable. With the rise of Big Data in the data management system, data engineers are also required to handle Big Data. They imbibe Big Data engineer skills for this purpose. Therefore, a data engineer works with several Big Data frameworks and NoSQL databases to manage Big Data.

Role of Big Data Engineer

The role of a Big Data Engineer is multifaceted, involving a combination of software engineering, data science, and systems architecture to manage, process, and analyze large data sets. Let's dive into each area to understand their responsibilities and contributions to an organization's data capabilities.

1. Work on Data Architecture

Big Data Engineers design the framework that dictates how data is stored, consumed, integrated, and managed across the organization. They ensure that the architecture supports the business requirements, allowing for efficient data storage, processing, and retrieval.

2. Data Pipelines

They build and manage data pipelines that automate data flow from various sources to the storage system or data warehouse. These pipelines include processes for collecting, cleaning, transforming, and loading data, ensuring that it is accessible and usable for analysis.

3. Design

Design responsibilities include creating scalable and efficient systems for data storage and analysis. This involves selecting the right database models, storage solutions, and computing resources to meet the organization's needs.

4. Data Warehouse

Big Data Engineers design and maintain data warehouses, which are centralized repositories of integrated data from one or more disparate sources. They ensure the data is consistently formatted and easily accessible for reporting and analysis.

5. Research

They constantly research new technologies, frameworks, and methodologies to improve data reliability, efficiency, and quality. This includes staying updated with the latest in machine learning algorithms, data processing tools, and big data technologies.

6. Enabling Efficient Data Collection

Engineers optimize the processes and technologies used for data collection to ensure that data is accurate, timely, and relevant. This might involve integrating various data sources or improving the performance of data collection tools.

7. Machine Learning

In some roles, Big Data Engineers also contribute to machine learning projects by preparing the data sets required for training models and deploying and monitoring those models in production environments.

8. Scalability

They ensure that data systems can scale up or down as needed to handle varying loads, which involves designing systems that can accommodate growth in data volume without performance degradation.

9. Collect and Store Data

Big Data Engineers are responsible for implementing solutions to efficiently collect and securely store data, ensuring it is organized and maintained to meet legal and business requirements.

10. Data Acquisition

This involves sourcing data from various origins, such as databases, web services, and third-party providers, and ensuring its compatibility with the organization's data systems. 

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11. Data Science

They often collaborate with data scientists by providing them with the tools and data necessary to conduct analyses, build models, and derive insights.

12. Defining Data Retention Policies

Big Data Engineers help establish policies determining how long data should be retained within the system, balancing legal requirements, storage costs, and business needs.

13. Develop Data Set Processes

They create processes for validating, storing, and accessing data sets, ensuring that users can rely on the quality and availability of the data for their analyses.

14. Developing Data Analysis Tools

Big Data Engineers may also be involved in developing tools that allow users within the organization to conduct their analyses, providing interfaces, libraries, or applications that simplify data access and manipulation.

15. Apache Hadoop

A foundational tool in big data, Big Data Engineers use Hadoop and its ecosystem (like Hive, Pig, and HBase) for distributed storage and processing of large data sets, designing systems that leverage its scalability and fault tolerance capabilities.

16. Performance Optimization

They continuously monitor and optimize the performance of data systems, ensuring that data flows efficiently through pipelines and that queries and analyses run within acceptable time frames.

17. Programming Languages

Big Data Engineers are proficient in several programming languages like Python, Java, Scala, and SQL. They use these languages to script data processing jobs, interact with data stores, and implement algorithms.

18. Provide Data Access Tools

They ensure that users across the organization have the tools and permissions necessary to access and analyze data, often involving setting up and managing user interfaces, APIs, or query languages.

Responsibilities of a Big Data Engineer

Big Data engineers have a spectrum of responsibilities starting from designing software systems, to collaborating and coordinating with data scientists. Given below are some of the duties of a Big Data Engineer:

  1. First and foremost, they are responsible for designing and implementing software systems. They also verify and maintain these systems.
  2. Big Data Engineers also build robust systems for ingestion and data processing.
  3. Extract Transform Load operations, known as the ETL process, is carried out by Big Data Engineers.
  4. They also research various new methods to obtain data and improve its quality. 
  5. Big Data Engineers are also responsible for building data architectures that meet the business requirements. They are responsible for generating a structured solution by integrating several programming languages and tools.
  6. Their primary responsibility is to mine data from plenty of different sources to build efficient business models.
  7. Finally, Big Data Engineers work with other teams, data analysts, and data scientists. 

Those were just a few of the key responsibilities of a Big Data Engineer. These responsibilities can only be carried out if you have a strong skill set. 

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Next up, let’s have a look at the Big Data Engineer skills.

Big Data Engineer Skills

A Big Data Engineer is required to be very skilled in many areas of expertise. Listed below are the top 7 Big Data Engineer skills you will need:

  1. Programming: Starting off, like most other technology-oriented job roles, out of all the Big Data Engineer skills, programming tops the list. A Big Data Engineer needs to have hands-on experience in any predominant programming language such as Java, C++, or Python.
  2. Database and SQL: After programming comes the in-depth knowledge of DBMS and SQL. This will help in comprehending how data is managed and maintained in a database. You need to know how to write SQL queries for any Relational Database Management system. Some of the commonly used database management systems for Big Data engineering are MySQL, Oracle Database, and the Microsoft SQL Server.
  3. ETL and Data warehousing: As mentioned earlier, one of the primary responsibilities of a Big Data Engineer is to carry out ETL operations. For this, you would need to know how to construct as well as use a data warehouse. 

As a Big Data Engineer, you will extract data from various sources, transforming them into meaningful information, and loading it into other data storages. Some of the tools used for this purpose are Talend, IBM Datastage, Pentaho, and Informatica.

  1. Operating System: The fourth skill that you require is knowledge of operating systems. Operating tools are the base for running Big Data tools. Hence a strong understanding of Unix, Linux, Windows, and Solaris is mandatory.
  2. Hadoop tools and frameworks: You must have experience with Hadoop based analytics. Hadoop is one of the most commonly used Big Data engineering tools, so it's understood that you need to have experience with Apache Hadoop based technologies like HDFS, MapReduce, Apache Pig, Hive & Apache HBase.
  3. Apache Spark: The sixth skill that you require, is to have worked with real-time processing frameworks like Apache Spark. As a Big Data Engineer, you will be dealing with enormous volumes of data, so for this, you need an analytics engine like Spark, which can be used for both batch and real-time processing. Spark can process live streaming data from several sources like Twitter, Instagram, Facebook, and so on.
  4. Data mining and modeling: The final skill requirement requires you to have experience with data mining, data wrangling, and data modeling techniques. Data mining and data wrangling include steps to preprocess and clean the data using various methods, find unseen trends and patterns in the data, and make it ready for analysis. 

Big Data Engineers examine massive pre-existing data to discover new insights through data modeling. Some of the tools used for this are Python, R, Rapid Miner, Weka, and KNIME.

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Qualifications for Big Data Engineer

The qualifications for a Big Data Engineer typically combine education, technical skills, and relevant work experience. These qualifications enable an individual to handle the complexities of large data sets and the technologies used to store, process, and analyze them. Below are key qualifications often sought after in Big Data Engineers:

1. Education

  • Bachelor’s Degree: A bachelor's degree in CS, IT, Engineering, or a related field is often a minimum requirement. It provides foundational knowledge in programming, data structures, and algorithms.
  • Master’s Degree: While not always required, a master's degree or higher in Data Science, Big Data Analytics, or a related field can benefit more advanced positions.

2. Technical Skills

  • Programming Languages: Proficiency in Python, Java, Scala, and SQL is crucial. These languages are commonly used for data manipulation, building data pipelines, and interacting with big data processing frameworks.
  • Big Data Technologies: Hands-on experience with Big Data technologies like Hadoop, Spark, Kafka, and NoSQL databases (e.g., HBase, Cassandra, MongoDB) is essential.
  • Data Processing: Skills in data processing frameworks and tools (e.g., Apache Beam, Flink) to handle streaming and batch data processing.
  • Machine Learning: Familiarity with machine learning algorithms and experience using ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn) can be a plus, especially for roles that overlap with data science.
  • Cloud Platforms: Experience with cloud services (AWS, Google Cloud Platform, Microsoft Azure) that offer big data processing capabilities and services.
  • Data Warehousing: Knowledge of data warehousing solutions and tools like Redshift, BigQuery, and Snowflake.

3. Work Experience

  • Relevant Experience: Previous work experience in data engineering, software development, or a related field is typically required, demonstrating practical skills in designing, implementing, and managing big data solutions.
  • Project Management: Experience with managing projects, including planning, execution, monitoring, and troubleshooting data pipelines and data storage solutions.

4. Soft Skills

  • Analytical Thinking: Ability to analyze complex data sets and derive insights.
  • Problem-Solving: Strong problem-solving skills to navigate challenges in data management and processing.
  • Communication: Good communication skills to collaborate with data scientists, analysts, IT teams, and business stakeholders.

5. Certifications

While not always required, certifications can demonstrate a candidate’s commitment and expertise in big data technologies. Examples include:

  1. Big Data Hadoop Certification Training Course
  2. Cloudera Certified Professional (CCP): Data Engineer
  3. AWS Certified Big Data – Specialty
  4. Microsoft Certified: Azure Data Engineer Associate
  5. Google Cloud Certified Professional Data Engineer

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Roadmap on How to Become a Big Data Engineer?

Career opportunities in the field of Big Data are endless as organizations rely on Big Data for crucial decision making. 

The average salary of a Big Data Engineer in the U.S is around $90,000 and ranges from $66,000 - $130,000. In India, the average salary is around Rs.7,00,000 and ranges from Rs.400,000 – Rs.14,00,000. 

In addition to the job role of a Big Data Engineer, there are a few more job profiles in this field, they are - Data architect, BI Architect, and Senior Big Data Engineer. 

To sum up the entire process on how to become a Big Data Engineer, let's look at the roadmap below:


Fig: How to become a Big Data Engineer?

As seen from the above roadmap, first, you need to complete your graduation and also fulfill the required skill set mentioned in Big Data Engineer skills. In addition to this, what can set you apart from the rest, is a Big Data certification course

If you are looking to become a Big Data Engineer, you can take up a few certifications, which will act as a catalyst in your transition to becoming a Big Data Engineer. Few relevant certifications a Big Data Engineer can opt for are:

  1. CCP Data Engineer
  2. IBM Certified Data Architect – Big Data
  3. Google Cloud Certified Data Engineer
  4. Big Data Master's Program from Simplilearn

So now, you must be wondering how Simplilearn can help you? 

If you're looking to make a career in the Big Data and Hadoop field, then the Big Data Engineer Master's Certification program, in collaboration with IBM provided by Simplilearn, will be a good fit. There are seven modules in this valuable course. 

You can learn about Big Data, Spark, PySpark, MongoDB, Cassandra, Scala, and others. Also, some of the essential tools covered in this course are Hadoop, Apache Spark, MongoDB, and Casandra, to name a few.  If you want to enroll for this course and start your career in Big Data, click on the following link: Big Data Engineer Master’s Program.


Fig: Big Data Engineer Master’s Program

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Career Growth Opportunity

The career growth opportunities for a Big Data Engineer are varied and promising, reflecting the increasing importance of big data in decision-making across industries. As organizations accumulate vast amounts of data, the demand for skilled professionals to manage, process, and extract valuable insights from this data grows. Here are some pathways and opportunities for advancement in the field:

1. Senior Big Data Engineer

After gaining experience, Big Data Engineers can move into more senior roles, taking on complex projects and mentoring junior engineers. These positions often involve strategic planning for data handling and processing and leading development teams.

2. Big Data Architect

Big Data Architects design the frameworks and systems that work with large data sets. This role requires a deep understanding of databases, software development, and system design principles. It's a natural progression for engineers looking to move into a more design-focused role.

3. Data Science/Data Analyst

Big Data Engineers with a strong interest in analytics and machine learning may transition into data science or data analysis. These roles focus more on extracting insights and creating predictive models from data. Additional training or education in statistics and machine learning techniques can support this career path.

4. Machine Learning Engineer

Moving into a Machine Learning Engineer role can be rewarding for those particularly interested in applying artificial intelligence (AI). This role focuses on creating data models that enable machines to learn and make predictions or decisions without being explicitly programmed to perform specific tasks.

5. Data Engineering Manager

With sufficient experience, a Big Data Engineer can advance to management positions, such as a Data Engineering Manager. This role involves overseeing a team of engineers and managing projects, resources, and timelines, ensuring data systems meet business needs.

6. Chief Data Officer (CDO)

In organizations where data plays a central role in strategy and operations, a Big Data Engineer with extensive experience and leadership skills could aspire to become a Chief Data Officer. This executive role focuses on governing the organization's data management strategy, ensuring data quality and accessibility, and leveraging data for business advantages.

7. Consultant/Advisor

Experienced Big Data Engineers may also work as independent consultants or advisors, offering their expertise to businesses on data strategy, architecture, and optimization projects. This path allows flexibility and the opportunity to work on various projects across industries.

8. Academia and Research

Transitioning into academia is another potential career path for those passionate about teaching and research. Big Data Engineers can pursue advanced degrees and conduct research in data science, big data technologies, and related fields, contributing to the body of knowledge and training the next generation of engineers.

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Future of Big Data

Here are several key trends and directions that are likely to shape the future of Big Data:

1. Increased Integration with AI and Machine Learning

Big Data will continue intertwining with AI and machine learning technologies, enabling more sophisticated analysis and insights. The ability to automatically analyze large datasets in real time will enhance predictive analytics, leading to smarter business decisions, personalized customer experiences, and innovative solutions to complex problems.

2. Growth of Edge Computing

As the Internet of Things (IoT) expands, there's a growing need to process data closer to where it is generated rather than relying on centralized data centers. This shift towards edge computing will reduce latency, save bandwidth, and improve the responsiveness of applications that rely on real-time data analysis, particularly in areas like autonomous vehicles, smart cities, and real-time monitoring systems.

3. Advancements in Data Privacy and Security

With the increasing data collection, privacy and security concerns are more prominent than ever. The future will likely see the development of more robust privacy-preserving technologies such as federated learning, differential privacy, and secure multi-party computation. These technologies can enable the analysis of sensitive data without compromising individual privacy.

4. Democratization of Data

Tools and platforms that simplify data analysis are becoming more user-friendly and accessible to people without specialized data science or programming training. This democratization of data means that more individuals and organizations can leverage big data insights for decision-making, potentially leveling the playing field across various sectors.

5. Augmented Analytics

Augmented analytics uses AI and machine learning to automate data preparation, insight generation, and explanation to augment human intelligence and contextual awareness. This trend is expected to accelerate, making analytics more accessible across organizations and reducing the time from data to insight.

6. Quantum Computing and Big Data

Although still in its infancy, quantum computing has the potential to revolutionize how we process and analyze big data. Quantum algorithms could dramatically reduce the time required for data processing and complex computations, opening up new possibilities for data analysis that are currently impractical with classical computing.

7. Increased Focus on Real-time Analytics

The demand for real-time analytics is growing, driven by applications that require immediate insights and actions, such as fraud detection, dynamic pricing, and predictive maintenance. This will necessitate improvements in data processing technologies and architectures to support faster analysis and decision-making processes.

8. Sustainability in Data Centers

As the environmental impact of digital infrastructure gains attention, future big data initiatives will likely emphasize sustainability. This could involve more energy-efficient data centers, carbon offsetting practices, and using renewable energy sources to power the massive computational resources required for big data processing.

9. Expansion of Data-as-a-Service (DaaS)

The DaaS model, which provides data on demand to users regardless of geographic or organizational separation from the data, is expected to grow. This will facilitate more flexible and scalable access to data resources, enabling companies to leverage external data sources more effectively.

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Are You Ready to Become a Big Data Engineer?

Reading this article, you had a brief introduction to the world of Big Data. Such as who a Big Data Engineer is, the various responsibilities of a Big Data Engineer, and Big Data Engineer skills. You also saw a roadmap on how to become a Big Data engineer. 

In addition to that, now you know exactly how Simplilearn can help you achieve your dream and kickstart your career in Big Data engineering By completing Post Graduate Program in Data Engineering Program.


1. Why is becoming a big data engineer a promising career choice?

Becoming a big data engineer is promising due to the exploding volume of data and the need to analyze it for business insights, efficiency improvements, and innovation. The role is in high demand across various industries, offering competitive salaries, opportunities for advancement, and the chance to work on cutting-edge projects.

2. What are the common tools and technologies used by big data engineers?

Big data engineers commonly use Hadoop, Spark, Kafka for data processing; NoSQL databases like MongoDB and Cassandra for data storage; Python, Java, and Scala for programming; and cloud platforms such as AWS, Google Cloud, and Azure for scalable infrastructure.

3. Is it possible to transition into big data engineering from a different career background?

Yes, it's possible to transition into big data engineering from different backgrounds, especially those with strong programming, analytics, or database management foundations. Gaining relevant skills through courses, certifications, and hands-on projects can facilitate this career shift.

4. What are the key differences between a big data engineer and a data scientist?

Big data engineers focus on designing, building, and managing the infrastructure and tools for data processing and analysis. Data scientists, on the other hand, analyze and interpret complex data to help organizations make informed decisions. Engineers enable data scientists by preparing the data architecture and tools.

5. What industries are in high demand for big data engineers?

Industries in high demand for big data engineers include technology, finance, healthcare, telecommunications, retail, and e-commerce. These sectors generate vast amounts of data and seek to leverage analytics for strategic advantages, operational efficiency, and enhanced customer experiences.

About the Author


Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

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