Data engineering is the practice of developing and constructing large-scale data collection, storage, and analysis systems. It is a vast field that has applications in almost every industry. Companies have the potential to collect vast volumes of data, but they need the proper people and technology to ensure that data scientists and analysts can use it. Have you always wanted to work in this field? If so buckle up because we’ll take you through all you need to know about data engineering, including who a data engineer is, what they do, their salary, and skill requirements, among others.
What is a Data Engineer?
A data engineer is an IT professional whose major responsibility is to prepare data for analytical or operational purposes. These software engineers are often in charge of creating data pipelines that connect information from several source systems. They combine, consolidate, and purify data before structuring it for use in analytics applications. They want to make data more accessible and maximize their company’s big data environment.
The amount of data an engineer works with varies depending on the business, particularly its size. The more complicated the analytics architecture and the more data the engineer is accountable for, the larger the organization. Several businesses, such as healthcare, retail, and finance, are more data-intensive.
Data engineers collaborate with data science teams to improve data transparency and enable businesses to make more reliable business decisions.
The Data Engineer Role
Data engineers collect and prepare data for data scientists and analysts to use. They have three key responsibilities:
#1. Generalists
Data engineers with a broad emphasis often work in small groups, collecting, ingesting, and analyzing data from start to finish. They may be more skilled than other data engineers, but they have less understanding of system architecture. A data scientist who wants to become a data engineer would be a good fit for the generalist role.
A generalist data engineer might work on a project for a small metro-area food delivery business that displays the number of deliveries made each day during the previous month and estimates the delivery volume for the following month.
#2. Pipeline-centric Engineers
These data engineers often work on a medium-sized data analytics team and on more complex data science projects that span distributed platforms. This position is more likely to be required in midsize and big businesses.
A regional food delivery company may embark on a pipeline-centric initiative to develop a platform for data scientists and analysts to search metadata for delivery information. They may examine the distance driven and time necessary for deliveries in the previous month, then utilize that data in a predictive algorithm to determine what it signifies for the company’s future business.
#3. Database-centric Engineers
These data engineers are in charge of building, maintaining, and populating analytics databases. This function is generally seen in larger organizations when data is dispersed across multiple databases. Engineers use extract, transform, and load (ETL) methodologies to develop table schemas and tune databases for efficient analysis. ETL is the process of copying data from multiple sources into a single destination system.
An analytics database would be a database-centric effort at a large, multistate, or nationwide food delivery business. In addition to building the database, the data engineer would write the code to transfer data from the primary application database to the analytics database.
What is Data Engineer Responsibilities?
Data engineers frequently collaborate with data scientists as part of an analytics team. Engineers deliver data in usable formats to data scientists, who use the information to perform queries and algorithms for predictive analytics, machine learning, and data mining applications. Data engineers also provide aggregated data to corporate leaders, analysts, and other end users for analysis and application to improve business operations.
Data engineers work with data that is both structured and unstructured. Structured data is information that can be formatted and stored in a formatted repository, such as a database. Unstructured data, such as text, photos, audio, and video files, does not fit into traditional data models. To handle both data kinds, data engineers must comprehend various approaches to data architecture and applications. The data engineer’s toolkit also includes a number of big data technologies, such as open-source data input and processing frameworks.
Academic Qualification and Required Skills for Data Engineering
Many organizations prefer people with a degree in computer science, information technology, or applied mathematics. Data engineers typically hold a degree in software engineering. Some even have degrees in mathematics or statistics, which helps them because they can apply what they’ve learned to tackle a variety of problems.
Prior expertise in constructing huge data warehouses capable of performing extraction, transformation, and loading (ETL) on large data sets will be advantageous. Furthermore, data engineers are proficient in programming languages such as Java, Python, SQL, and Scala.
What is Data Engineer Skills?
There are multiple data engineering skills and they include the following:
#1. Languages for Programming
Knowledge of coding languages such as Javascript, Python, and Scala.
#2. SQL Expertise
SQL is yet another data language. A data engineer should be able to express the many types of complications in SQL using techniques such as correlated subqueries and window functions. A data engineer should also be able to read and comprehend database execution plans. They should comprehend the program’s processes, how indices function, the various join methods, and the distributed dimension.
#3. Architectural Projections
A data engineer should be familiar with libraries, tools, resources, platforms, the nuances of various database features, computation, stream processors, properties, workflow orchestrators, message queues, serialization formats, and other related technologies.
#4. Data Modeling Techniques
They should be well-versed in normalization and denormalization tradeoffs, entity-relationship modeling, and dimensional modeling.
#5. ETL (Extract, Transform, and Load)
This data integration procedure enables data engineers to combine data from multiple sources to generate a single data source. This single source of data is then placed in a data warehouse. Data engineers should be able to create systemized ETL that can adapt to change.
#6. Data Storage
You should be able to store data as a data engineer. As a result, while building data solutions for a corporation, you must decide whether to employ a data warehouse or a data lake.
#7. Cloud Computing
Understanding cloud computing and cloud storage is critical as enterprises increasingly replace physical servers with cloud services.
#8. Big Data Tools
Data engineers may occasionally work with and handle large amounts of data. Kafka, Hadoop, and MongoDB are popular tools and technologies.
How to Become a Data Engineer
Data engineers often have a background in computer science, engineering, applied mathematics, or another subject connected to information technology. Because the profession needs extensive technical knowledge, prospective data engineers may discover that a boot camp or certification will not be enough to compete. According to PayScale, most data engineering positions demand at least a relevant bachelor’s degree in a related discipline.
You should have prior experience with numerous programming languages, such as Python and Java, as well as knowledge of SQL database architecture. A boot camp or certification can help you adapt your resume to data engineering employment if you already have a background in IT or a related area such as mathematics or analytics. For example, if you’ve previously worked in IT but haven’t had a specific data role, you may enroll in a data science boot camp or obtain a data engineering certification to demonstrate that you have the abilities in addition to your other IT experience.
If you don’t have a history in technology or IT, you may need to enroll in an intensive program or invest in an undergraduate program to demonstrate your competency in the subject. If you have a bachelor’s degree but it is not in a relevant discipline, you can check into master’s degrees in data analytics and data engineering.
It will ultimately rely on your situation and the types of professions you are interested in. Take the time to read through job postings to understand what firms are looking for, and you’ll have a better notion of how your history fits into that function.
Data Engineer Salary 2023
As of Feb 13, 2023, the average annual pay for a data engineer in the United States is $122,672 a year. The average additional cash compensation salary for a data engineer is also $26,372 and then the average total compensation salary for a data engineer is $150,629. This salary can also differ when it comes to the qualification and experience of the data engineer.
The entry-level data engineer salary is around $77,783 per year. They typically have only a few years of experience, from one to three years. Mid-Level data engineer salary is about $106,748/ year. These experts generally have five to nine years of experience in the field. Lastly, the senior data engineer salary is $117,826/ year. Senior data engineers often have ten or more years of experience in the field and are in charge of supervising and assigning tasks to junior data engineers.
Data Engineer vs. Data Scientist
They both collaborate on projects. Data engineers compile and organize company data stored in databases and other formats. They also create data pipelines that provide data to data scientists. All of that data is used by data scientists for analytics and other projects that improve business operations and outcomes.
Data scientists and data engineers have different skill sets and areas of focus. When it comes to data engineers, they may not always have a specific emphasis; they are typically adept in multiple areas and well-rounded in their knowledge and skills. Data scientists, on the other hand, frequently have specialized areas of focus. They are more interested in exploratory data analysis. Data scientists tackle novel, big-picture problems, while data engineers put the pieces in place.
Data Engineer vs. Data Architect
The jobs of data engineer and data architect are intertwined and frequently confused. Senior visionaries who translate business requirements into technical requirements and develop data standards and principles are known as data architects. They visualize and develop the corporate data management structure of a company. Data engineers collaborate with the data architect to construct and maintain the data systems described by the data architect’s data architecture.
What does a Typical Day Look like for a Data Engineer?
A data engineer’s primary goal is to transform raw data into something usable and accessible before presenting it to an organization. Not only that, but they must design, construct, test, blend, manage, and optimize data from many sources. They build the infrastructure that will produce this data. The goal is to build data pipelines that run smoothly. Furthermore, they design complicated queries to ensure that the data is easily accessible.
A data engineer’s normal day can vary based on their company.
What do I need to be a Data Engineer?
A bachelor’s degree in computer science, software or computer engineering, applied math, physics, statistics, or a related discipline is required for entry into this field. Most entry-level roles will also require real-world experience, such as internships.
Is Data Engineering a Good Career?
While the characteristics of a job that make it “excellent” will always be subjective, data engineering is a high-demand profession with above-average pay and job stability.
Do Data Engineers do Coding?
Coding is a required ability for data engineers, as it is for other data science professions. Aside from SQL, data engineers employ a variety of additional programming languages for a variety of jobs. There are other programming languages that may be utilized in data engineering, but Python is without a doubt one of the greatest.
In Conclusion
The need for data engineer positions has skyrocketed in recent years. Companies are aggressively seeking data engineers to help them with their data problems. This skill set is in high demand, and it is far from oversaturated, as in other fields. Individuals who learn these abilities have a good chance of making a good living. We have provided this material to assist you in progressing in this area of work. Good luck!
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