What does a big data engineer actually do? How much can they earn? And what tools do they use? Find out in this post.
It's time to talk about the elephant in the room: big data engineering. Big data engineers don't get as much airtime as data scientists and analysts, but they are an irreplaceable part of the data economy. While they share many of their skills with other data-related roles, a data engineer's primary focus is on making data accessible to all types of users. This allows organizations to use it for a variety of different tasks. Without data engineers, we would be stuck.
In this article, we offer a first introduction to big data engineering. We've covered all the basics you need to know, including:
- What is data engineering and what does a big data engineer actually do?
- What is the difference between a Big Data Engineer, a Data Analyst and a Data Scientist?
- How much do Big Data engineers earn?
- How to become a big data engineer
- What tools does a big data engineer use?
- Conclusion and further reading
What does a big data engineer actually do? Let's find out
1. What is data engineering and what does a big data engineer actually do?
Data engineering is essential for any technology-focused organization. While the specifics of the role vary from job to job, their primary role is to design, test, and maintain big data architectures, data pipelines, warehouses, and other processing systems. The ultimate goal of a data engineer is the retrieval, storage and distribution of data throughout the enterprise.
Okay, let's admit it... On paper, this doesn't sound as flashy as something like data analytics (which can literally predict the future). asdata office) essential. Without engineers to bridge the gap between the chaos ofbig dataand the order of relational databases, data scientists and analysts have failed to unlock the hidden potential of data.
While data engineers may not be known for making amazing discoveries without working their magic and transforming the data into a format anyone or anyone else can use, this way data engineering becomes infinitely more attractive. Data engineers are like the wizards of the data world without their skills.nothing will happen!
What does a Big Data Engineer do?
Okay, we've covered the basics. But what does a data engineer's day job look like? What are your duties and responsibilities? To give you an idea, here are some real job advertisements.
Big data engineering manager.
- Design, build and manage scalable ETL (Extract, Transform, Load) systems and pipelines for multiple data sources
- Manage, enhance and maintain existing data warehouse and data lake solutions
- Optimize and improve what already existsdata qualityand data governance processes to improve performance and stability
- Build custom tools and algorithms for data science and analytics teams (and other data-driven teams across the organization)
- Work closely with business intelligence teams and software developers to define strategic goals like data models.
- Work closely with the broader IT team to manage the overall business infrastructure.
- Discover the next generation of data-centric technologies to extend business capability and maintain a competitive edge
Ideal Skills for Big Data Engineers
- Critical thinking, excellent communication, teamwork and problem solving.
- Completed degree in computer science (or comparable professional subject)
- Several years of experience in software development or data management.
- Strong technical background with knowledge of multiple programming languages and a general love of writing code
- Hands-on experience with Python and SQL, as well as big data technologies like Apache Stack
- Experience dealing with relational database management systems, e.g. PostgreSQL, MySQL
- Understanding of batch and real-time data integration, data replication, data streaming, virtualization, etc.
While a big data engineer is likely to have a natural gift for things like problem solving, more practical skills (e.g. different technologies) can be learned. So don't panic if you look at this list and think, "I don't know anything about this!" Instead, take it as a challenge: pick one from the list first, then explore.
2. What is the difference between a Big Data Engineer, a Data Analyst and a Data Scientist?
The terms data scientist, data analyst and data engineer are often used interchangeably. However, these are different functions, so let's define them more precisely. Although they share many data-related skills,However, each role has its own function.. Understanding this is important for distinguishing between them. Let's see these features now.
What is the role of a big data engineer?
The main job of a big data engineer is to manage and maintain big data infrastructures.
This includes collecting, storing and distributing data within an organization. Fundamentally, the role of a data engineer has a strong developmental aspect. Therefore, you will find that data engineers often start their careers as software developers. Review section one for more details on what the job entails.
What is the role of a data analyst?
The main role of a data analyst is to extract information from data to support decision making..
While the data analyst role covers a wide range of data-related tasks (from collecting and cleaning to structuring data), it is primarily concerned with identifying and interpreting trends to solve well-defined business problems. Data analysts can be part of a larger business intelligence team or integrated into a specific department (with expertise in a specific domain).
A data analyst can use customer usage data to determine which features of a product need improvement. Or they use data to develop more efficient supply chain strategies.
Data analysts rely heavily on the infrastructure that big data engineers build and maintain. While they can also manipulate these structures to some extent (for example, retrieving data from relational databases using SQL), they likely don't have the same knowledge of these technologies as a big data engineer.
What is the role of a data scientist?
While a data analyst obtains information from the data,A data scientist's main job is to build the methods to extract these insights from big data..
Your role is similar to that of a data analyst, but at a much higher level. A data scientist will develop entirely new models or analysis techniques for others to use. And while analysts know a specific business area, e.g. Sales or product design, a data scientist observes from a helicopter. They have an overview of the broader business strategy and focus on opportunities that benefit the entire organization. Often, data scientists have executive management or leadership (as well as data science) experience.
Data scientists are often talented data engineers. It turns out that data engineering is a complex and time-consuming task. Having a dedicated engineer saves data scientists a lot of time, although the two roles still work together. The blurring of lines between data scientists and data engineers is why the terms are often used interchangeably.
Despite the common abilities, the key points of this section are the different roles of each role. While there are always some crossovers, to recap:
- a data engineermanages and maintains large data infrastructures
- a data analystextract information from datato inform decision-making
- First, a data scientistdevelop the methods to obtain this knowledge
3. How much do Big Data engineers earn?
Then something simpler... money! What is the salary of a big data engineer? Of course, the answer to this question varies by professional experience, job title, geographic location, and company.
But if you take an average of estimates from various job and salary comparison sites (glass door,Löhnskal,salary expert, YWage. with) We found itBig data engineers in the US earn an average of $108,000. The actual value can be even higher when things like bonuses or geographic weighting are taken into account (eg data engineers in Europe can earn even more).
How does a big data engineer's salary compare to data analysts and data scientists? According to Salary.com, data analysts earn in the United Statesaverage $77,000, while data scientists earnan average of $132,000. While these are just estimates, none of these numbers are bad... and all are above the US national average.
For more detailed breakdowns, you canLearn more about a data engineer salary here.
4. How to become a big data engineer
You know what a big data engineer does, and you know how their work differs from data analysis and data science. You even know how much you could earn. But how do you become one? In this section, we briefly highlight some things you can do to secure a job in this field:
- Get a relevant title: Data engineers must be suitably qualified. You will likely need a degree (master's or higher) in a field such as computer science, software engineering, physics or applied mathematics.
- Consider a certified course:If you already have a degree or don't want to go down this route, another option is to take a certified online course to refresh your relevant skills. This could be in an area like data analysis, machine learning or software development. you will findHere's a comparison of some of the best data analytics certification programs..
- Gain work experience:Landing a job as a big data engineer usually requires some work experience. Perhaps you were a software developer or data analyst or worked as an intern? You can also create a portfolio of your work.
- Get familiar with the databases:Databases are the building blocks of all big data architectures. Make sure you have a good understanding of the theory and train yourself in using tools like SQL and database management systems (such as those described in Section 5).
- Develop some broader skills:There are many tools you can use as a data engineer. You don't have to be an expert on all of them, but it helps to understand what species exist and how they interact with each other.
- Stay open to all job offers:More often than not, data engineers start their careers in a variety of roles, whether as software developers, data specialists, or through an academic path. Be open to any data engineering job early in your career, even if it's not the job you've always dreamed of!
Learn more about what it takes to become a big data engineer here.
If you've decided this is the path for you... where do you go next? First, consider playing around with some of the tools big data engineers often use. This will give you a good idea of whether data engineering might be right for you. The basics include things like MS Excel and the basics of system design. However, big data engineers also use a number of different technologies. Some of them are:
- Python (and other programming languages)
- ETL Tools
- SQL e NoSQL
- PostgreSQL (or other database management system)
- Apache Spark (and Hadoop to a lesser extent)
- Amazonas S3
Let's take a closer look at these now.
Python (and other programming languages)
The Python programming language is an increasingly essential requirement for anyone working with data. Versatile and easy to learn, it has become a popular language in recent years, pushing the boundaries of experience. That's exactly why it's so useful: By using the same language, those working in different data domains can speed up the integration of their work.
Of course you can also use other programming languages if you already know them. This may includeJavaoScale. Python is simply the most popular. He canLearn more about Python here.
Extract, Transform, and Load (ETL) tools are a group of technologies used to transfer data from one system or infrastructure to another. Essentially, they allow users to extract (pull) data from different sources, consolidate (transform) that data into new formats, and then transfer (load) it into a new database or system.
ETL can be performed using programming languages such as Python or using proprietary software designed specifically for the task, for example,Quiteotalend.
SQL e NoSQL
SQL (Structured Query Language) is a domain-specific language used to communicate with relational databases. Meanwhile, NoSQL refers to structures that store data in a non-relational format.
As tools, this simply means that big data engineers can use SQL to communicate with data stored in a predefined tabular format (relational databases), but they can also work with unstructured big data stored in the so-called "list format". shopping". . This is usually a document or file (non-relational database).
PostgreSQL (or other database management system)
postgresqlis a free and open source relational database management system. It supports SQL and a common type of tool used by many data engineers. Although PostgreSQL has its origins in the mid-1990s, it has proved popular in the digital age and is widely used as a data store for many popular web and mobile applications (which, as we know, are the source of many forms of data ). . ).
Alternatives to PostgreSQL include other open source solutions such asmysql. Corporate systems are also available such as Microsoft SQL andoracle database.
Apache Spark (and Hadoop to a lesser extent)
When your work involves large amounts of big data, individual databases on a computer are often not enough. So you have to invest time in distributed computing systems like Apache Spark or Hadoop.
These tools distribute large data sets across clusters of computers. Legacy Hadoop is quite expensive to implement and complex to use, but it is still widespread due to its legacy usage. However, more recently it has been superseded by Apache Spark, a much faster system better suited to new techniques such as machine learning.
There are numerous business tools for big data engineers. If we were to list them all, we'd be here for a while! However, an example is Amazon S3. The Amazon S3 web service allows users to store and retrieve any amount of data from anywhere on the Internet. Essentially, developers get access to the same infrastructure that Amazon uses to run its global network of websites.
This illustrates how evolving technologies are being adapted to modern requirements. Many big data engineers even specialize in Amazon S3 and other Amazon Web Services (AWS).
While this list is just a small sampling of the tools you might find, we encourage you to venture out and explore some of your own. Ask yourself: what would you like to specialize in? And from there... What tools can help you?
6. Summary and further reading
In this post, we explore what a big data engineer actually does. We take you on a tour of the field and see how big data engineering differs from data analysis and data science. We've learned that being a data engineer can make a good living, and we've shared some tips for getting your new career started!
While data engineering isn't as well known as other data-related roles, it's an in-demand job that can be very rewarding.
To learn more about what a career in data entails, tryfree 5 day short data analysis course?
We can also recommend the following:
- What does a data scientist actually do in finance?
- Data Bootcamp vs. Data Grade: Which Is Best For You?
- What is the difference between machine learning and deep learning?
What skills are needed for big data engineer? ›
- Multi-Cloud computing. A data engineer needs to have a thorough understanding of the underlying technologies that make up cloud computing. ...
- Visualization. ...
- Machine Learning and AI. ...
- NoSQL. ...
- Data Pipelines. ...
- Hyper Automation. ...
- Programming. ...
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.What is a big data engineer salary? ›
Salary Ranges for Big Data Engineers
The salaries of Big Data Engineers in the US range from $68,931 to $155,000 , with a median salary of $90,000 . The middle 57% of Big Data Engineers makes between $90,000 and $110,000, with the top 86% making $155,000.
Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.Does big data engineer require coding? ›
As a data engineer, you must have strong coding skills as you'd need to work with multiple programming languages. Apart from Python, other popular programming skills include . NET, R, Shell Scripting, and Perl. Java and Scala are vital as they let you work with MapReduce, a vital Hadoop component.What big data skills are most in demand? ›
- 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. ...
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 big data engineer a stressful job? ›
Is data engineering stressful? Many 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.Where do big data engineers get paid the most? ›
Average big data engineer salary at Google: $201K. Average big data engineer salary at Microsoft: $184K. Average big data engineer salary at Amazon: $181K. Average big data engineer salary at Apple: $170K.What is the salary of entry level big data engineer in USA? ›
How much does an Entry Level BIG DATA Engineer make? As of Feb 1, 2023, the average annual pay for an Entry Level BIG DATA Engineer in the United States is $97,757 a year.
What are data engineer skills? ›
- SQL. SQL serves as the fundamental skill-set for data engineers. ...
- Data Warehousing. Get a grasp of building and working with a data warehouse; it is an essential skill. ...
- Data Architecture. ...
- Coding. ...
- Operating System. ...
- Apache Hadoop-Based Analytics. ...
- Machine Learning.
Who is a Data Engineer? Data Engineers procure data from numerous resources and convert. the same followed by building and managing systems that generate this data. They transform and clean the procured data for data scientists and analysts to scrutinize. They make the data viable by writing complex queries.What does a data engineer do in simple terms? ›
Introduction to the role of data engineer. A data engineer develops and constructs data products and services, and integrates them into systems and business processes.Can big data engineers 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.Can I learn big data without coding? ›
Everyone can learn data science and get started with analytics and extracting business insights from data. This is also true for people from a non-technical background who have no prior coding experience.Do data engineers need SQL? ›
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.How do you put big data skills on a resume? ›
- Strong knowledge of the Hadoop ecosystem and its core frameworks, including HDFS, YARN, MapReduce, Apache, Pig, Hive, Flume, Sqoop, Oozie, Impala, ZooKeeper, and Kafka.
- Proficient in SQL-based technologies (MySQL, Oracle DB, etc.)
Becoming a Big Data Analyst
While Big Data jobs often call for a degree in math, statistics, finance, economics, or computer science, that's not always the case. Because the field is evolving just as fast as the data it analyzes, the role requires professionals to upskill continuously.
Because of the often technical requirements for Data Science jobs, it can be more challenging to learn than other fields in technology. Getting a firm handle on such a wide variety of languages and applications does present a rather steep learning curve.How do I prepare for a big data engineer interview? ›
- Create a Stellar Data Engineer Resume. ...
- Practice Coding. ...
- Brush Up on Data Engineering Fundamentals. ...
- SQL. ...
- Data Structure and Algorithms. ...
- System Design. ...
- Python. ...
- Take Mock Interviews to Prepare for Behavioral Interview Rounds.
Do data engineers work long hours? ›
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. For this extensive background knowledge, data engineers earn $50.69 every hour for their services.How many years does it take to become a big data engineer? ›
To become a data engineer, you usually need a bachelor's degree and 2-4 years of experience. The most common jobs before becoming a data engineer are software engineer, hadoop developer, and java developer. It takes an average of 3-6 months of job training to become a data engineer.Is big data engineer easy? ›
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.What is the salary of big data engineer in Amazon? ›
Amazon Big Data Engineer salary in India ranges between ₹ 12.0 Lakhs to ₹ 52.0 Lakhs with an average annual salary of ₹ 27.4 Lakhs. Salary estimates are based on 27 Amazon latest salaries received from various employees of Amazon.Which certification is best for big data engineer? ›
- 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.
How much does a BIG DATA Engineer make? As of Jan 23, 2023, the average annual pay for a BIG DATA Engineer in the United States is $130,429 a year. Just in case you need a simple salary calculator, that works out to be approximately $62.71 an hour. This is the equivalent of $2,508/week or $10,869/month.What is a good starting salary for a data engineer? ›
How much does an Entry Level Data Engineer make in the United States? The salary range for an Entry Level Data Engineer job is from $102,341 to $146,751 per year in the United States.Is data engineer a high paying job? ›
A mid-career Data Engineer with 4-9 years of experience earns an average salary of ₹13.4 Lakhs per year, while an experienced Data Engineer with 10-20 years of experience earns an average salary of ₹22.1 Lakhs per year.Is big data engineer a good career? ›
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.What is required to be a data engineer? ›
Data engineers are expected to know how to build and maintain database systems, be fluent in programming languages such as SQL, Python, and R, be adept at finding warehousing solutions, and using ETL (Extract, Transfer, Load) tools, and understanding basic machine learning and algorithms.
What skills are needed for data entry? ›
- Typing speed.
- Typing accuracy.
- Communication skills.
- Time management.
- Attention to detail.
- Ability to research and collect data.
- Understanding of basic software.
Meet with individuals ad-hoc to work through any bugs or blockers. Write/test/run code and algorithms on the data to make sure they run and work as expected. And plan tasks for the team for the upcoming days and weeks and review decisions with management.What motivates data engineer? ›
Their ability to unearth interesting and unusual data patterns, and develop predictive and analytical models, helps to discover new solutions that can lead to positive outcomes such as cost-saving. However, data scientists are not purely driven by business goals. Instead, they are motivated by experimentation.What software do data engineers use? ›
Data engineers use tools such as Python, Spark, Kafka, SQL, Tableau, Snowflake, etc., for various big data activities such as data analytics, data processing, etc.What is data engineer with example? ›
Data engineering helps make data more useful and accessible for consumers of data. To do so, ata engineering must source, transform and analyze data from each system. For example, data stored in a relational database is managed as tables, like a Microsoft Excel spreadsheet.Is big data engineer hard? ›
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 Python enough for data engineer? ›
Python is also the go-to language for data scientists and a great alternative for specialist languages such as R for machine learning. Often branded the language of data, it's indispensable in data engineering. As a data engineer, I can't imagine doing my job without Python.What skills are required for big data testing? ›
- Knowledge of SQL and PLSQL scripting languages.
- Graduation in Engineering, Computer Science, or other similar fields of study.
- Knowledge of programming languages like Python.
- In-depth understanding of business processes.
- Knowledge of budgeting and cost monitoring.
- Analytical and communication skills.
Java, Python, R, and Scala are commonly used in big data projects. In a series of articles, I am describing these languages briefly and the reasons for their popularity among data scientists.Can a non IT person learn big data? ›
Data Science is only for persons with an IT background. It is a persistent myth that many people believe. Although it is true that some IT professionals seek to advance their skills in analytics, this field is not only open to people with a background in programming and IT.
Can you become a big data engineer without a degree? ›
Since there is no set university curriculum specifically for data engineering, it is still possible to become a data engineer without a degree.Is SQL required for data engineer? ›
SQL is a must-have skill for data engineers. They use the querying language to perform essential tasks like modeling data, extracting performance metrics, and developing reusable data structures.Is SQL enough for data engineer? ›
There are several SQL types that data engineers might focus exclusively on at some point (Advanced Modelling, Big Data, etc.), but getting there requires learning the basics of this technology. That's why all companies, from giants like Apple to small businesses, need their data engineers to be experts in using SQL.