How to Become a Big Data Engineer in 2024

How to Become a Big Data Engineer -A step by step learning path to launch a successful career as a big data software engineer in 2024.

How to Become a Big Data Engineer in 2024
 |  BY ProjectPro

The Big Data industry will be $77 billion worth by 2023. According to a survey, big data engineering job interviews increased by 40% in 2020 compared to only a 10% rise in Data science job interviews.  


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Big Data Engineer - The Market Demand

big data engineer

An organization’s data science capabilities require data warehousing and mining, modeling, data infrastructure, and metadata management. Most of these are performed by Data Engineers. Industries generate 2,000,000,000,000,000,000 bytes of data across the globe in a single day. Increased data analytics and management adoption has increased the demand for data engineers, which is five times more than data scientists. The demand and even the average salaries of big data engineers are higher than those of Data Scientists. Many organizations are willing to pay 20-30% more to their Data Engineers than to Data Scientists. 

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Google Trends shows the large-scale demand and popularity of Big Data Engineer compared with other similar roles, such as IoT Engineer, AI Programmer, and Cloud Computing Engineer. 

Google Trends

Who is a Big Data Engineer?

Big Data refers to the massive volumes of data which is no longer possible to manage using traditional software applications. Automated tools are developed as part of the Big Data technology to handle the massive volumes of varied data sets. Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. They are responsible for changing the design, development, and management of data pipelines while also managing the data sources for effective data collection.

Big Data Engineer vs. Data Analyst vs. Data Scientist 

The most popular data science job roles in demand are Data Engineer, Data Analyst, and Data Scientist. These roles have overlapping skills, but there is some difference between the three. The following table illustrates the key differences between these roles. 

Parameter

Data Engineer

Data Analyst

Data Scientist

Primary Role

Data preparation and gathering to construct and manage the entire data architecture 

Data analytics to discover the patterns and trends for effective data-driven decision-making 

Interpretation of the complex data and organization of the Big Data infrastructure 

Key Skills

  • Data warehousing and ETL 

  • Advanced knowledge of SQL or another database language 

  • Hadoop-based analytics 

  • Data visualization 

  • Advanced programming knowledge in Python or another language 

  • Data pipelining 

  • Data Warehousing 

  • Statistics and scripting 

  • Data visualizations 

  • Programming knowledge 

  • Google Analytics/Adobe 

  • Data mining 

  • Data optimization 

  • Hadoop-based analytics 

  • Machine Learning/Deep learning principles 

  • Programming knowledge on R, Python, or another programming language 

Primary Responsibilities

  • Building data pipelines

  • Construction and maintenance of Big Data architectures 

  • Deploy machine learning and statistical models 

  • Gathering and organization of the data sets 

  • Pre-processing and gathering of the data 

  • Statistical analysis on the data sets 

  • Data acquisition and management 

  • Data reporting and visualization 

  • Development of operational data models

  • Big data infrastructure management 

  • Data analytics and optimization 

  • Data integration 

  • Ad-hoc analysis 

What does a Big Data Engineer do?

Big Data Engineer performs a multi-faceted role in an organization by identifying, extracting, and delivering the data sets in useful formats. A Big Data Engineer also constructs, tests, and maintains the Big Data architecture. The key responsibilities are deploying machine learning and statistical models, resolving data ambiguities, and managing of data pipelines.  Big Data Engineer identifies the internal and external data sources to gather valid data sets and deals with multiple cloud computing environments. 

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Big Data Engineer Job Description

Big Data Engineers are expected to perform the following tasks for their organization: 

  • Gather raw data from internal and external data sources

  • Process raw data to prepare meaningful data sets for the organization 

  • Ensure the validity of the data collected and processed

  • Improve scalability and security of the data pipelines

  • Design and develop data applications using Big Data tools for effective data organization and management 

  • Extract, Transform and Load the data to the identified Big Data frameworks and tools 

  • Write scripts and SQL queries 

  • Collaborate with engineering, design, networking, and other teams to integrate the work with the production systems. 

  • Transform unstructured data in the form in which the data can be analyzed 

  • Develop data retention policies

Skills Required to Become a Big Data Engineer 

Big Data Engineer Degree - Educational Background/Qualifications

  • Bachelor’s degree in Computer Science, Information Technology, Statistics, or a similar field is preferred at an entry level. 

  • A Master’s degree in Computer Science, Information Technology, Statistics, or a similar field is preferred with 2-5 years of experience in Software Engineering/Data Management/Database handling is preferred at an intermediate level. 

  • Software Engineers with data management knowledge and specialization usually take up the role of a Big Data Engineer. You can also become a self-taught big data engineer by working on real-time hands-on big data projects on database architecture, data science, or data engineering to qualify for a big data engineer job. 

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Technical Skills Required to Become a Big Data Engineer

  • Database Systems: Data is the primary asset handled, processed, and managed by a Big Data Engineer. You must have good knowledge of the SQL and NoSQL database systems. SQL is the most popular database language used in a majority of organizations. NoSQL databases are also gaining popularity owing to the additional capabilities offered by such databases. You shall know database creation, data manipulation, and similar operations on the data sets. 

  • Data Warehousing: Data warehouses store massive pieces of information for querying and data analysis. Your organization will use internal and external sources to port the data. You must be aware of Amazon Web Services (AWS) and the data warehousing concept to effectively store the data sets. 

  • Machine Learning: Big Data, Machine Learning, and Artificial Intelligence often go hand-in-hand. Data Scientists use ML algorithms to make predictions on the data sets. Basic knowledge of ML technologies and algorithms will enable you to collaborate with the engineering teams and the Data Scientists. It will also assist you in building more effective data pipelines. 

  • ETL Tools: Extract, Transfer, and Load (ETL) pulls data from numerous sources and applies specific rules on the data sets as per the business requirements. It then loads the transformed data in the database or other BI platforms for use. As a Data Engineer, you will extensively use ETL in maintaining the data pipelines. You should have an understanding of the process and the tools. 

  • Programming Skills: The choice of the programming language may differ from one application/organization to the other. You shall have advanced programming skills in either programming languages, such as Python, R, Java, C++, C#, and others. 

  • Algorithms and Data Structures: You should understand your organization’s data structures and data functions. Basic knowledge of algorithm design and data structures is essential to effectively define checkpoints and manage Big Data frameworks.

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Non-Technical Skills Required to Become a Big Data Engineer

  • Communication Skills: Big Data engineers work with other teams, such as engineering and design, networking, development, etc. As a Big Data Engineer, you will also work with other Big Data Engineers, Data Analysts, Data Scientists, and likewise. You must have the skills to communicate effectively with fellow team members and colleagues. You may also interact with the clients to understand their business requirements and share the big data project updates. Communication is critical to streamlining these discussions, and you should have good verbal and non-verbal communication skills. 

  • Analytical Skills: You will come across several data-related and operational risks and issues in your role as a Big Data Engineer. Also, you will work with the Data Analysts to design and development the prediction algorithms and functions on the data sets. Analytical skills will be helpful in effectively performing these tasks.  

  • Presentation Skills: Big Data Engineers also present the data-driven findings to the internal and external stakeholders. You should have good presentation skills to engage the customers and stakeholders in your presentations. You should explain the intricate technical details adequately to make sure the audience understands the insights shared. 

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How to Become a Big Data Engineer?

Big Data technologies are now being used in multiple industries and business sectors. The retail sector, for instance, is using Big Data analytics to perform customer analytics. The sales and marketing strategies implemented today follow the data patterns and trends identified by data professionals. Similarly, the construction sector is using Big Data to anticipate the one-site risks, weather patterns, etc., to schedule the project activities. Healthcare has immense scope for Big Data and is already being used in the diagnosis and treatment of patients. 

To become a Big Data Engineer, you should target a diverse range of skill-set and business domains to gain an edge in the market. For example, at an entry-level, you can join internships in multiple organizations with the scope and use of Big Data in their organization. Simultaneously, you can work on your database language, programming skills, and knowledge of algorithms and data structures. You will learn these concepts in your academic courses. However, suppose you do not have an educational background in Computer Science or Information Technology. We suggest you learn the fundamentals on Algorithms and Data Structures, Programming, Database Management, Software Development, etc., from tons of free online resources. And next step would be to put the theory into practice by working on diverse hands-on big data projects. 

SQL, for instance, is the database language to query the database for performing data-related operations. You can work on a broad range of analytics projects to practice writing complex SQL queries and master the database language needed for the big data engineer jobs.  You should also look to master at least one programming language. Python, R, and Java are the most popular languages currently. Properties and attributes, such as robustness, ease of learning, easy development, and scalability, make these languages a preferred choice for any data enthusiast. 

At intermediate and senior levels, you will need advanced programming and statistical abilities. Your experience in previous organizations will help develop and enhance these skills. 

As a Big Data Engineer, you shall also know and understand the Big Data architecture and Big Data tools.  Hadoop, Kafka, and Spark are the most popular big data tools used in the industry today. Prior learning and knowledge of these tools will distinguish you from the rest of the candidates. You will get to learn about data storage and management with lessons on Big Data tools. Hadoop, for instance, is open-source software. You can explore the tool and its features to understand the basic know-how of the tool. Having familiarity with one or more big data tools is a must to land a top gig as a big data software engineer. 

You shall look to expand your skills to become a Big Data Engineer. Usually, Software Engineers take up the role of Big Data Engineers. These resources have academic/professional backgrounds in software development or lifecycle management. To be a Big Data Engineer, you should focus on specializing in database language or data management tools to have an edge. 

You must also work on soft skills alongside the expansion of your technical expertise. If you lack communication, collaboration, or effective presentation, it is implausible for the organizations to prefer you over other candidates. 

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Big Data Engineer Salary

Big Data Engineer Salary By Experience

The salary of a Big Data Engineer at an entry-level is $112,555 annually in the US. With an experience of 2-3 years, the average salary is $116,591 per annum. Senior Big Data Engineer with over five years of experience has an average salary of $148,216 per annum.

At an intermediate level, the salary of a Big Data Engineer in India is INR 1270K. Big Data Engineers at senior level with over 8 or 10 years of experience earn INR 1600K on average. 

Big Data Engineer Salary By Location 

Salaries can significantly vary for a Big Data Engineer from one country to another. Other factors, such as experience, specialization, skills, company, etc., also play an essential role. 

In India, the average salary of a Big Data Engineer is INR 830K. Most of the Big Data Engineering jobs in the country are in Bangalore, the IT capital of the country. 

In the UK, the average salary of a Big Data Engineer is £43,725 per annum. Liverpool and London are the cities with excessive demands for Big Data Engineers. 

The average salary offered to a Big Data Engineer in Canada is CAD 80K.

The average salary in the US is $116,591; however, significant variations are present from one state to the other. Variations are also seen from one city to the other in the United States. For instance, in NYC, the average salary of a Big Data Engineer is $123,070 annually, while it is $118,122 in Los Angeles. In Seattle, the average salary of a big data software engineer further drops to $110,479. 

The following table shows the salary distribution for a Big Data Engineer in different countries. 

Country

Average Salary of a Big Data Engineer (Per Annum)

India

INR 830K

UK

£43,725

Canada

CAD 80K

United States

$116,591

Australia

AUD 103,346

Singapore

S$62,648

Germany

€ 60,632

 

Tips to Prepare a Big Data Engineer Resume

You can follow the tips and recommendations below to develop an impressive and engaging resume for the role of a Big Data Engineer.

Highlight In-Demand Skills

You need to possess a diverse range of skills to qualify and increase your chances to get hired as a Big Data Engineer. 

You must highlight the in-demand skills to distinguish from the other candidates and get noticed. For example, Machine Learning is one of the skills excessively in-demand and is essential for a Big Data Engineer. Similarly, advanced programming skills in R or Python give an edge for the role.  You can also highlight your previous experience with Business Intelligence (BI) solutions and applications in customer data analytics, industry trend analytics, etc.

Don’t Mention Every Tool.

Organizations have their preference for the use of Big Data frameworks or database languages. You must focus on including the skills rather than explicitly listing all the tools you know or have worked on.  For example, Apache has developed numerous Big Data tools, such as Hadoop, Spark, and others. Knowledge and experience with either of these tools will lead to developing skills in Big Data handling and management. Some organizations may look for skills in Big Data management or specifically Apache Spark. 

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Becoming a Big Data Engineer - The Next Steps

Big Data technologies are high on-demand with enormous opportunities for Big Data Engineers in a variety of sectors. IT, Retail, Sales & Marketing, Healthcare, Manufacturing, Education, etc., are some of the business domains with massive demand for Big Data Engineers. With an increase in demand, there is an increase in competition. More and more candidates are acquiring skills in advanced programming, Machine Learning algorithms, database languages, data analytics, statistics, etc.

You should look to diversify your skills while targeting advanced knowledge of specific tools or languages. For example, you shall look to have advanced knowledge of a particular programming language along with skills in statistics, database language, etc., rather than diversifying your skills in multiple programming languages only. Having a versatile big data skillset will improve your chances of fulfilling the demands and expectations of the hiring managers of the organizations. There is no better way to learn all the big data skills required for the job than to learn by doing. ProjectPro offers a personalized learning path of diverse solved big data and data science projects that will help you get started on your journey towards becoming a big data engineer.

 

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