A big data engineer is defined as a technically-proficient professional who can convert structured, unstructured, and semi-structured data into actionable insights for a business, along with developing the strategies, tools, and models to make this possible. This article explains the job role of a big data engineer and its skill and salary expectations for 2022.
Table of Contents
- Big Data Engineer Job Description: Roles and Responsibilities
- Big Data Engineer Key Skill Requirements in 2022
- Big Data Engineer Salary in 2022
Advancements in technology and the internet have caused a significant increase in the amount of raw data generated daily. This pooling of raw data comes from credit card transactions, online transactions on e-commerce websites, social media engagements, website traffic, and sensor readings from IoT devices.
Responsibilities of a Big Data Engineer
These data pools, generated at very high speed and in massive volume, are known as big data. On its own, big data collected is primarily unstructured and useless. However, with the proper process, one can use big data to optimize business use cases, reduce risk, understand customer behavior, predict trends, and increase company revenue streams. This creates a dire need for big data engineers who can transform big data into a usable form.
Big data engineer role
The big data engineer is one of the most important employees in any large organization – that is, organizations whose daily transactions and activities generate bulky data that one can analyze for further development. The big data engineer employed by the company would be an information technology expert tasked with designing, building, testing, and servicing an intricate data processing system created to work with the particular data set of that company.
A big data engineer is responsible for evaluating the company’s data. They create a system or an algorithm that collects the big data as it is being churned out. Big data exists as a mixture of structured and unstructured data. The traditional models of storing and organizing data do not apply to big data. Therefore, the ultimate role of the big data engineer is to convert this raw, scattered data to a form where a data analyst can use the data to draw meaningful conclusions.
If the big data engineer carries out this role effectively, it will propel the organization closer to where efficiency, profitability, and well-planned scalability become a norm. The big data engineer lays the foundation for an organization to maximally tap out its potential through adequate handling of big data.
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10 critical responsibilities of a big data engineer
Big data engineers have primary responsibilities in every organization they find themselves in. These are:
1. Designing the architecture of the database infrastructure
At the core of big data engineering is designing the architecture of the database infrastructure. This is usually the first foundational step taken in data management. However, the structure designed is meant to act as a guide to the engineer for further control of big data, creating room for adjustments. So, the design of the data processing system must be in line with the organization’s needs, which is both an initial and continuous process.
2. Enabling efficient data collection
The big data engineer has to obtain data from the right channel that one would enter into the data processing system designed. Data collection can be from any source, depending on the organization. It could be from an application server database, internet of things (IoT) sensors, user-facing applications, etc.
3. Developing data analysis tools
A big data engineer is primarily a developer capable of programming. They are responsible for programming customized data analysis tools they would integrate into the data processing process. These tools may be used by the big data engineer or other data team members such as the data analysts. The tools include integrations, databases, warehouses, and analytical systems.
4. Testing and maintenance of data pipeline
The big data engineer must regularly test and maintain the data pipeline. This data pipeline is a system that transfers data from the source location to the target storage location. In data pipeline testing, each segment of this pipeline is tested by the big data engineer or together with the data testing team for reliability. Big data engineers should monitor the pipeline as the automated parts may need to be modified to fit the ever-changing data requirements.
5. Managing both data and metadata
The big data engineer is responsible for managing the data stored in a warehouse, the cloud, or a data lake. This data which can be structured or unstructured, must be properly managed by the engineer using the appropriate systems. The big data engineer is also tasked with managing and updating metadata which describes and gives further information about the data.
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6. Deploying of ML models
Data scientists design machine learning (ML) models for an organization. This model is now integrated into the big data production environment by the big data engineer.
The purpose of machine learning deployment is for the ML model to start making practical, informed decisions based on the data input. The model is first fed with data coming from the source or stored in the warehouse. It configures the data attributes, manages computing resources, and oversees data monitoring tools. The models should be able to gain insights from existing and past data, discover hidden repetitive patterns and make recommendations for different outcomes.
7. Provisioning data access tools
The big data engineer may have to provide tools to access data depending on who needs to pull data from storage. If the engineer is working with data scientists or data analysts who may be able to access this data directly, there will be a lesser need for these access tools. In some instances, nontechnical professionals may access the data and need the means to do so. Provisioning these tools to view data, create reports, and interact with the data falls on the shoulder of the big data engineer.
8. Conducting research
Big data engineers can also carry out research in the industry they work. This will help to identify new ways to get valuable data, solve any arising problem and gain a clearer picture of the industry, the customer base, and the real-world meaning of the data they are working on. This gives the data engineer a better perspective and increased idea flow while helping the organization meet its goals.
9. Task and workflow automation
Big data engineers should be able to identify parts of the data processing and pipeline where human effort can be cut down and be entirely or partially automated through workflow automation. This reduces the recurring cost of production, diverts human resources to more practical problems, and increases creativity.
10. Optimizing data platform performance
Big data engineers are responsible for maintaining an optimal performance standard of the big data platform. They must frequently monitor the process and use the necessary structures to improve any lagging section. Some techniques used by big data engineers include database optimization techniques and efficient data ingestion. Database optimization techniques can be data partitioning, breaking data into independent subsets with a partition key to ease data retrieval.
Big data engineers must be able to handle big data. Simultaneously storing and processing them to usable forms as they enter the data processing system.
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According to the Dice 2020 Tech Job Report, the global demand for data engineers increased by 50% in just one year. This means there is a shortage of data engineers and big data engineers to help manage the ever-increasing burden of big data across organizations.
Key Skill Requirements for Big Data Engineers
The big data engineer must be able to collect, store, manage and maintain the big data infrastructure of a company. To do this effectively, anyone working as a big data engineer or aspiring to be one must have a particular skill set. Generally, the role of a big data engineer is closely intertwined with software development. However, a more specific skill set that one must obtain includes:
1. Knowledge of programming languages
A big data engineer must be able to understand, use, and implement all the common programming languages. They may not necessarily be the best, but there must be an acceptable level of competency in those languages. This is important to understand the machines you will be working with, build and customize data access tools, build data pipelines, and code the ETL process. Big data engineers must know programming languages like Python, Java, Scala, C, etc.
2. An understanding of database management systems
A big data engineer must fully understand database management systems, primarily SQL and NoSQL databases. While big data is stored chiefly using NoSQL, it would be challenging to appreciate and understand it without first knowing what SQL is. SQL or Structured Query Language is a relational database management system that stores structured data in multiple related tables.
SQL is a skill whose necessity cuts across the board for every data professional. NoSQL, on the other hand, is a more advanced database system that can store and query large amounts of data (big data). NoSQL tools such as Cassandra and MongoDB run on multiple nodes and can store semi-structured or unstructured raw data in the form of tabular columns or even graphs. A big data engineer should know which database best suits the use case and write targeted queries for the database.
3. Analytical and problem-solving skills
Big data engineers must have good analytical mindsets. They should be able to understand complex data, use analytical tools and draw valuable conclusions. Analytical skills also correlate with mathematical and statistical abilities. Following closely behind is the importance of problem-solving skills. This is because big data in its raw form is unstructured and problematic. Problem-solving allows big data engineers to extend the limits of their creativity to create solutions to problems.
4. Skills in ETL data warehousing
ETL means Extract, Transform, and Load – i.e., the steps taken to collate information in a data warehouse. There could be a mix of different data types, all of which are transformed and finally loaded into the target database lake or warehouse. This is done using various ETL or warehousing tools, and the big data engineer uses these tools. Thus, understanding ETL is non-negotiable. All ETL tools operate on the same principle, so understanding how to use one covers up for a large part of all others. Data from multiple sites are extracted.
5. The ability to visualize data
Data visualization is an integral part of big data. Data must be presented in an appealing form that easily conveys the message. Data visualization goes hand in hand with creativity. Beyond the ability to present data in an attractive format, the big data engineer must be able to understand different ways of data presentation.
6. Good interpersonal skills and teamwork
The big data engineer is not an island in the ocean. Their role in the organization is closely linked with other professionals like the data analyst, the business intelligence unit, the software developers, product managers, etc. They must all work together to achieve a common goal, eliminate repetition and resource wasting, outline a strategy, and exchange creative ideas. The big data engineer should be able to interact appropriately with teammates.
7. Degree, certifications, and experience
Becoming a big data engineer requires mastering many hard skills and extensive education. One of the best ways to achieve this is by getting a bachelor’s or master’s degree in the related fields. These include computer science, business data analytics, statistics, etc., which give a strong foundation where one can now build other skills. Most companies also require a minimum bachelor’s degree for core positions such as big data engineer.
In recent times, certifications have become a key trend in data science and analytics. They affirm that the bearer has attained some widely accepted level of expertise. So, while data analytics certifications are not core to being a big data scientist, they certainly help one stand out from the crowd. Experience is equally crucial for landing big data engineering roles, which can be gotten from freelancing, interning, personal practice, etc.
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It’s no news that being a big data engineer is one of the most lucrative positions you can find. The job description comes with an annual pay and benefits that most dream of. Moreso, as you advance your skill sets, there is always room for growth, improvement, and salary increase.
Across the United States, the average salary of a big data engineer is about $104,463. According to the Glassdoor report from several big data engineers (last updated on July 5, 2022), this is a baseline salary outside additional cash compensations one might expect. Cash compensation can range anywhere between $2,342 to $30,427.
When starting as a big data engineer, an individual can expect to be paid about $112,500 yearly. With further growth and promotions to senior data engineer roles, they are likely to receive an increase in annual salary by $27,500 totaling an average of $136,000.
The role of a senior big data engineer is similar and intersects with that of the lead data engineer. A lead data engineer must have strong project management and organizational skills. They should have a good history as a big data engineer. The average annual salary attached to this career level is about $135,000, similar to that of a senior data engineer.
The principal data engineer is the next level in the big data engineer career ladder. The principal data engineer must be able to create and maintain optimal data pipeline architecture. The average annual take-home salary, excluding cash compensations, amounts to $156,200. Climbing further up this ladder takes you through job positions like director of data engineering sciences, senior director, and even vice president of data engineering.
In addition to the attractive salary package with multiple growth paths, big data engineers in cities like San Francisco typically make a take-home bonus of up to $15,000, representing 13% of their yearly salary and 4% higher than the national average. Cities like Chicago, on the other hand, record a low-paying average of about $68,931 compared to the national average.
Similar jobs like big data engineering also offer lucrative salaries and might be worth the consideration. Some of these roles include:
- Data engineers, who earn about $122,759
- Software development engineers earn an average of $119,930, with software engineers earning about $110,033 and software developers about $100,000
- An application security engineer or test engineer who earns about $88,250
- Product managers and system administrators, who make $90,000 and $85,013, respectively
- Business analysts and data analysts who earn an average of $82,847 and $67,457
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With global spending on big data poised to grow by 12.8% between 2021 and 2025 (as per IDC’s 2021 Worldwide Big Data and Analytics Spending Guide), the role of a big data engineer will be crucial for organizations. These professionals can make sense of various disparate data sets and extract insights from unexpected sources. From manufacturing to healthcare and governments – nearly every industry today is looking to hire big data engineers for their business analytics needs. As a result, big data engineering is a highly worthwhile career path for the foreseeable future.
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Big Data Engineer salary in India ranges between ₹ 4.2 Lakhs to ₹ 21.0 Lakhs with an average annual salary of ₹ 8.5 Lakhs. Salary estimates are based on 3.3k salaries received from Big Data Engineers.How can I become a data engineer in 2022? ›
- Earn a bachelor's degree in a relevant field. ...
- Master relevant skills. ...
- Pursue additional certifications or courses. ...
- Become proficient at programming. ...
- Study cloud computing. ...
- Advance professionally. ...
- Additional skills.
Highest reported salary offered as Big Data Engineer is ₹35lakhs. The top 10% of employees earn more than ₹23lakhs per year. The top 1% earn more than a whopping ₹34lakhs per year.Does big data require coding? ›
Coding is required. For working professionals who code: Coding is required in Data Science, and you can pick it up. There is a learning curve in Data Science because, along with code, you will also need to unlearn and relearn mathematics and business.What 3 skills are involved in data analyst? ›
- Critical Thinking.
- Statistical programming languages.
- Data visualization.
- Public speaking.
- Machine learning.
- Data warehousing.
- Big Data Engineer.
- Software Architect.
- Blockchain Engineer.
- DevOps Engineer.
- Cloud Architect.
- Full-Stack Developer.
- Artificial Intelligence (AI) Engineer.
- Product Manager.
Hadoop Developer salary in India ranges between ₹ 3.3 Lakhs to ₹ 10.5 Lakhs with an average annual salary of ₹ 5.5 Lakhs.Is big data good for career? ›
Due to the various challenges in learning these skills, the need for professionals in this field continues to increase, making the big data field a sought-after career path.Is big data in demand in 2022? ›
The big data market in India was valued at INR 132.63 Bn in 2021. It is expected to reach INR 558.24 Bn by 2027, expanding at a CAGR of ~26.80% during the 2022 - 2027 period. At present, India is one of the top 10 countries in the market, with over 600 data analytics firms.
In 2021, data engineers can run big jobs very quickly thanks to the compute power of BigQuery, Snowflake, Firebolt, Databricks, and other cloud warehousing technologies.
Lappas says, "The job is very difficult. It's an unsexy job, but it's super-critical. Data engineers are kind of like the unsung heroes of the data world. Their job is incredibly complex, involving new skills and new tech.Is big data engineer stressful? ›
These factors force data engineers to work long, irregular schedules that take a toll on their well-being. In fact, 78% of survey respondents wish their job came with a therapist to help manage work-related stress.Is big data engineer a stressful job? ›
Data engineers are so stressed out that 78% wish their job came with a therapist. Yikes. Solving this is no easy task and requires a detailed understanding of why data engineers are burnt out.Why Data Engineer salary is high? ›
Average Data Engineer Salary
The high demand and the importance of this position across industries have created incredible earning opportunities for skilled data engineers.
Need of SQL in Data Science
SQL is also the standard for the current big data platforms that use SQL as their key API for their relational databases.
Python provides a huge number of libraries to work on Big Data. You can also work – in terms of developing code – using Python for Big Data much faster than any other programming language. These two aspects are enabling developers worldwide to embrace Python as the language of choice for Big Data projects.Is Python mandatory for big data? ›
Whether you want to become a data analyst or make the big leap to data scientist, learning and mastering Python is an absolute must!What qualities should a data engineer have? ›
- Coding. ...
- Data warehousing. ...
- Knowledge of operating systems. ...
- Database systems. ...
- Data analysis. ...
- Critical thinking skills. ...
- Basic understanding of machine learning. ...
- Communication skills.
Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and make an informed decision.Which skill is best for data analyst? ›
- SQL. Structured Query Language, or SQL, is the standard language used to communicate with databases. ...
- Statistical programming. ...
- Machine learning. ...
- Probability and statistics. ...
- Data management. ...
- Statistical visualization. ...
- Machine learning engineer. This specific branch of artificial intelligence is ideal for those who have a passion for computer science and desire a career in a fast-moving and exciting industry. ...
- UX designer. ...
- Robotics engineer. ...
- Data scientist. ...
- Cloud engineer.
Average salary for a Information Technology IT in India is 8 Lakhs per year (₹66.7k per month).What are big data skills? ›
In Big Data Market, a professional should be able to conduct and code Quantitative and Statistical Analysis. One should also have a sound knowledge of mathematics and logical thinking. Big Data Professional should have familiarity with sorting of data types, algorithms and many more.Can fresher get job in big data? ›
The answer to this question is a very big YES. There is no place in IT where freshers are not required whether it be an old technology like mainframes or latest technology like Big Data.What is salary of AWS? ›
An entry-level AWS professional makes between $70,000 and $90,000 per year. A senior AWS professional earns between $135,000 and $166,000 per year. AWS salaries can vary across different jobs.What are 4 benefits of big data? ›
- Customer Acquisition and Retention. ...
- Focused and Targeted Promotions. ...
- Potential Risks Identification. ...
- Innovate. ...
- Complex Supplier Networks. ...
- Cost optimization. ...
- Improve Efficiency.
In the future, big data analytics will increasingly focus on data freshness with the ultimate goal of real-time analysis, enabling better-informed decisions and increased competitiveness.Which degree is best for big data? ›
B.S. in Computer Science: This degree is a natural fit for a career in data science with its emphasis on programming languages.Which IT field is in demand 2022? ›
1. Software developer -- also known as software development engineer or software engineer. Job description. Software developers are engineers who build software programs, applications, networks and OSes.Is Hadoop still in demand in 2022? ›
The Hadoop and Big Data Market is said to reach $99.31 billion in 2022 attaining a CAGR of 28.5%.
To refine these processes, data engineering plays a pivotal role. Hence the demand for data engineers is skyrocketing. AIMResearch estimates that the data engineering market will grow at a CAGR of 36.7% in the next five years, growing from USD 18.2 billion in 2022 to USD 86.9 billion in 2027.Is big data still in demand? ›
Because of its numerous benefits, big data analytics is undoubtedly in high demand. The enormous growth is indeed due to the wide range of industries in which Analytics is used. The image below shows the various job opportunities available in various domains.Which country is best for big data engineer? ›
- San Jose, California. ...
- Average Salary : $132,355 per annum. ...
- Bengaluru, India. ...
- Average Salary: Rs. ...
- Geneva, Switzerland. ...
- Average Salary: 180,000 Swiss Fr (Franc) to 200,000 Swiss Fr (Franc) per annum.
- Berlin, Germany. ...
- Average Salary: €11,000 to €114,155 per annum.
- IBM Data Engineering Professional Certificate. ...
- Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate. ...
- IBM Data Warehouse Engineer Professional Certificate. ...
- Meta Database Engineer Professional Certificate.
Python is the most widely used data science programming language in the world today. It is an open-source, easy-to-use language that has been around since the year 1991. This general-purpose and dynamic language is inherently object-oriented.What is the role of big data engineer? ›
A big data engineer is an information technology (IT) professional who is responsible for designing, building, testing and maintaining complex data processing systems that work with large data sets.How many hours do big data engineers work? ›
Data engineers typically work a full-time schedule at 40 hours a week, Monday to Friday. They may be required to work extra hours or on weekends, too.Are big data engineers in demand? ›
You'll rely on your programming and problem-solving skills to create scalable solutions. As long as there is data to process, data engineers will be in demand.What is a big data engineer called? ›
This is the job of big data engineers — also known as data scientists, statisticians, and computer and information research scientists.Can a big data engineer work from home? ›
As a remote data engineer, you focus on collecting, storing, and organizing large amounts of information. You work from home to design, develop, and maintain systems for the mining, warehousing, and processing of data.
A mid-career Data Engineer with 4-9 years of experience earns an average salary of ₹13.1 Lakhs per year, while an experienced Data Engineer with 10-20 years of experience earns an average salary of ₹21.4 Lakhs per year.Which one is better java or data science? ›
There is no right or wrong answer to this but knowing Java is definitely beneficial because it provides a host of other services when working with data science applications. Many top companies like Spotify, Uber, continue to use Java along with Python to host business-critical data science applications.Is big data engineer a developer? ›
Big data engineers are skilled as software developers, and they have to be proficient in coding, an excellent data scientist, and an engineer all at the same time. This is a multi-faceted role, and any big data engineer could find themselves performing a range of tasks on any day of the week.Is data engineer a good career in 2023? ›
Data Engineer Salary. For the analytical mind, both positions offer a highly rewarding and lucrative career.What qualifications do I need to be a data engineer? ›
Anyone who enters this field will need a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. You'll also need real-world experience, like internships, to even qualify for most entry-level positions.Do big data engineers code? ›
A big data engineer is an information technology (IT) professional who is responsible for designing, building, testing and maintaining complex data processing systems that work with large data sets.Is SQL required for data engineer? ›
Being a data engineer requires you to combine a lot of skills: a deep understanding of data structures, knowledge of different data storage technologies, familiarity with distributed and cloud computing systems, etc. Among all these skills, SQL and database knowledge are fundamental to data engineering.Is python required for data engineer? ›
A data engineer needs to learn various tools like SQL, Python, etc., to know how to connect to a database and retrieve data. Here are two project ideas to help you learn how to perform data ingestion on big data.How do I become a big data engineer? ›
Big data engineers hold at least a bachelor's degree, with most also having an advanced degree, such as an online master's in business data analytics. The added years of study are crucial for learning the myriad technical skills that a big data engineer needs.
There are five traits that you'll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more. Is the information correct in every detail? How comprehensive is the information?Why data engineer salary is high? ›
Average Data Engineer Salary
The high demand and the importance of this position across industries have created incredible earning opportunities for skilled data engineers.
- Apache Spark.
- Apache Kafka.
- Amazon Redshift.
Choosing a career in the field of Big Data and Analytics will be a fantastic career move, and it could be just the type of role that you have been trying to find. Professionals who are working in this field can expect an impressive salary, with the median salary for Data Scientists being $116,000.Is big data in demand? ›
Big data analytics is used everywhere
The enormous growth is indeed due to the wide range of industries in which Analytics is used. The image below shows the various job opportunities available in various domains.