Big Data Engineer: Job Description, Key Skills, and Salary in 2022

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. 

Big Data Engineer Job Description: Roles and Responsibilities

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. 

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. 

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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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Key Skill Requirements for Big Data Engineers in 2022 

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. 

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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Big Data Engineer Salary in 2022

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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Takeaway 

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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