Data Engineering:
What is a Data Engineer and How Do I Become One?

Here are five things you should know about data engineering:

1. Data engineers design, build and install data systems for machine learning and analytics. These professionals execute tasks such as data acquisition, data transformation, data processing, database management, raw data management, data storage, and data modeling.

2. In an e-commerce context, data engineers help online retailers make smarter decisions by creating data pipelines that merge data sets for analysis. 

3. Data engineering is an in-demand and lucrative career. Many e-commerce organizations require data engineers to help them make sense of all the big data that flows in and out of their organization. 

4. Data engineers can undertake formal training or teach themselves the required programming skills for a high-paying job role. 

5. Integrate.io is a data warehousing integration solution that helps data engineers create pipelines with little or no code. Engineers can also create custom advanced pipelines. 

Data scientists are responsible for analyzing e-commerce data and using it for various purposes. However, they need good quality data to accomplish complex tasks, such as forecasting trends about customers and inventory. That's where data engineers come in. Data engineering is the science of collecting and validating information (data), so data scientists can use it. 

Data engineering is the seventh-best job in America for 2022, according to Glassdoor, and data engineers earn, on average, $115,267 a year. With an excellent pay scale and high demand, data engineering can be a lucrative career option, especially in the e-commerce sector. However, you might consider the following information before committing to a data engineering career.

Integrate.io is a new data warehouse integration platform for e-commerce that data engineers can use to move data between sources with little or no code. Schedule an intro call to learn more. 

Data Engineering: What Are the Responsibilities?

Data engineers set up and maintain the data infrastructures that support e-commerce information systems and applications. They might work with something small, like a relational database for a small online retailer—or something big, like a petabyte-scale data lake for a Fortune 500 retail company.

As part of their responsibilities, data engineers design, build and install data systems. These systems fuel machine learning and e-commerce AI analytics. They also develop information processes for a whole host of data tasks. These tasks include data acquisition, data transformation, and data modeling. 

Whether it's a one-person show or a larger team, the field of data engineering includes the following positions:

• The Data Architect: Data architects design data management systems for an entire e-commerce organization or specific parts of it. Their work allows data systems to ingest, integrate, and manage all the required sources of data for business insights and reporting. The work of a data architect may rely on in-depth knowledge of SQL, NoSQL, and XML, among other systems and tools.

• The Database Administrator: Database administrators help design and maintain database systems for e-commerce companies. They ensure that database systems function seamlessly for all users in an organization. Database administrators optimize databases for speed. They also ensure that updates don't interfere with workflow and that sensitive information is secure.

• The Data Engineer: Data engineers understand several programming languages used in data science. These include the likes of Java, Python, and R. They know the ins and outs of SQL and NoSQL database systems. They also understand how to use distributed systems such as Hadoop. Having such a wide expanse of knowledge allows them to work with data architects, database administrators, and data scientists. In fact, sometimes, they can perform all those roles themselves. Essentially, data engineers are responsible for building a robust, integrated data infrastructure for an organization.

Data Scientists vs. Data Engineers: What's the Difference? 

1. Data scientists use statistical modeling and other tools to analyze e-commerce data. Data engineers focus on building the required infrastructure for generating and preparing data for analysis.

2. Data scientists work closely with key decision-makers in an e-commerce enterprise to carve out a data strategy. Data engineers work closely with data scientists to make high-quality data available to them.

3. Data scientists are responsible for generating e-commerce insights. Data engineers are responsible for building and maintaining pipelines that feed data to the data scientists. 

Data scientists carry out many responsibilities in modern e-commerce enterprises. For instance, helping online retailers show targeted ads or product recommendations. Their work gives companies tremendous competitive advantages. For example, retaining more customers through data analytics.

Data scientists specialize in statistical modeling and machine learning technology. They develop graphical displays, dashboards, and other methods to share vital business intelligence with decision-makers in an e-commerce organization. However, every data scientist needs access to quality data—hence, the need for data engineers.

Data engineers create data pipelines that connect data from one system to another. They are also responsible for transforming data from one format to another so that a data scientist can pull data from different systems for analysis. Even though data engineers aren't as visible as data scientists, they're just as important (if not more so) when it comes to e-commerce data analysis. As a simple analogy, if data scientists are train conductors, data engineers are the builders of the railway network that gets the trains from A to B.

Now, let's say the train conductor wants to deliver a payload somewhere that doesn't have an established railway line. The conductor needs the railway network builders to connect the train to the new destination. The railway builders and architects will study the terrain. They'll decide if it's better to go around, go over, or tunnel through any mountains in the way. They'll probably build bridges over rivers. They'll use all the tools available to create a railway line that connects the train to the new destination.

Data scientists interact with data by writing queries. They are responsible for creating dashboards for insights and developing machine-learning strategies. They also work directly with decision-makers to understand their information needs and develop strategies for meeting them. Data engineers build and maintain the data infrastructures that connect an e-commerce organization’s data ecosystems. These infrastructures make the data scientist's work possible.

Read more: Data Engineer vs. Software Engineer: What is the Difference?

What Skills Should a Data Engineer Have?

Data engineers need to acquire a variety of skills related to programming languages, databases, and operating systems. As a data engineer, it is important to keep in mind that you'll never feel like you know everything, but you will know "enough." More importantly, you'll learn how to find information and acquire new skills when needed.

Ultimately, the acquisition of skills and knowledge is a career-long process. Yes, you'll need to be an expert in particular topics and programming languages (as your job requires). But you also need to be an expert at looking up information. For example, you might need an SQL statement to perform a specific action. Similarly, you might need to brush up on MapReduce when analyzing a large data set featuring a parallel, distributed algorithm on a cluster.

Broadly speaking, here are 13 knowledge areas you'll develop during your career as a data engineer:

Data engineers need expertise in the following programming languages as a bare minimum:

• SQL: To set up, query, and manage some e-commerce database systems. SQL is not a "data engineering" language per se, but data engineers will likely need to work with SQL databases frequently.

• Python: To create data pipelines, write ETL scripts, set up statistical models, and perform analysis. This language is particularly important for ETL, data analysis, and machine learning applications.

• R: To analyze data and set up statistical models, dashboards, and visual displays. Like Python, R is an important language for data science and data engineering. It's especially useful for data analysis and machine learning applications.

Knowledge of these scripting languages allows data engineers to troubleshoot and improve database systems. It also allows them to optimize the business insights tools and machine-learning systems they’re working with. Data engineers could also benefit from being familiar with Java, NoSQL, Julia, Scala, MATLAB, and TensorFlow.

Data engineers need to know how to work with a wide variety of data platforms. SQL-based relational database systems (RDBMSs) like MySQL, PostgreSQL (a hybrid SQL and NoSQL database), and Microsoft SQL Server are particularly important. For example, they should feel comfortable using SQL to build and set up database systems. Data engineers should also develop skills working with NoSQL databases such as MongoDB, Cassandra, Couchbase, and others.

Data engineers should be comfortable using data warehousing integration systems like Integrate.io that extract, transform, and load data to a target system for data analysis. That helps e-commerce decision-makers identify patterns and trends in data sets.

4) Data Warehouses

After extracting information from various business systems, data engineers may need to prepare the information for integrating it with a data warehouse system. Data integration is crucial if they want to query it for deep insights. Cloud-based data warehouses form the backbone of the most advanced business intelligence data systems. Data engineers should understand how to set up a cloud-based data warehouse like Snowflake, Amazon (AWS) Redshift, or Microsoft Azure. They should be adept at connecting a wide variety of data types to a warehouse and optimizing those connections for speed and efficiency. 

Data warehouses can only work with structured information, such as information in a relational database. Relational database systems store data in clearly identified columns and rows. Meanwhile, data lakes can work with any type of data. This includes unstructured information, such as streaming data. BI solutions can hook up to data lakes to derive valuable insights. For this reason, many companies are incorporating data lakes into their information infrastructures.

For applying machine learning algorithms to unstructured data, it is important to know how to integrate data and connect it to a business intelligence platform. 

Data engineers develop essential data pathways that connect various information systems. Therefore, data engineers should have a good understanding of data pipelines. They should know how they help different parts of an information network communicate with each other. For example, they should be able to work with REST, SOAP, FTP, HTTP, and ODBC and understand strategies for connecting one information system or application to another as efficiently as possible.

A data ingest refers to the extraction of e-commerce data from different sources. During the extraction process, the data engineer needs to pay close attention to the formats and protocols that apply to the situation while extracting the data swiftly and seamlessly.

After storing the data, data scientists establish the important connections between information sources. These sources could be data warehouses, data marts, data lakes, and applications. Establishing connections between sources could involve exposing the company’s data to advanced machine-learning algorithms for business intelligence. Data engineers must understand how this process works to support data scientists in their jobs.

Many business intelligence and machine learning platforms allow users to develop beautiful, interactive dashboards. These dashboards showcase the results of queries, AI forecasting, and more. Creating dashboards is usually the responsibility of data scientists. However, data engineers may assist the data scientists in this process. Many BI platforms and RDBMS solutions allow users to create dashboards via a drag-and-drop interface. Knowledge of SQL, R, and Python can come in handy, though. It will enable a data engineer to assist the data scientist in setting up dashboards that fit their needs.

Machine learning is primarily the domain of data scientists. However, because data engineers are the ones who build the data infrastructures that support machine learning systems, it’s crucial that they feel comfortable with statistics and data modeling. Moreover, not all e-commerce organizations will have a data scientist, especially smaller online retailers. Therefore, it’s good to understand how to set up BI dashboards, deploy machine learning algorithms, and extract deep insights independently.

The machine learning systems of the future will likely be UNIX-based. It is due to requirements for hardware root access and the need for additional functionality that Windows and Mac OS don’t provide. Therefore, data engineers will want to get familiar with these operating systems if they haven’t done so already.

12) Data Governance

Data engineers need to be aware of data governance frameworks when moving data between sources and destinations. That's because e-commerce organizations can receive hefty fines for not complying with legislation such as GDPR, CCPA, and HIPAA. When creating data pipelines, engineers should consider sensitive customer data and whether that information adheres to data protection principles. 

13) Data Loss 

Data loss is one of the biggest challenges for data engineers when transferring information between sources and destinations. Using a data warehouse integration platform like Integrate.io can prevent data loss and improve data quality as it moves from one location to another. 

Integrate.io provides data engineers with an e-commerce data warehouse solution that doesn't require complicated code. Schedule an intro call to learn more. 

Read more: Data Science vs. Data Engineering: What You Need to Know

How Do I Learn to Be a Data Engineer?

There's no clear path to becoming a data engineer. Although most data engineers learn by developing their skills on the job, you can acquire many of the skills you need through self-study, university education, and project-based learning.

Whether you learn to be a data engineer at a university or on your own, there are many ways to reach your goal. Let's take a look at four ways people develop data engineering skills:

A University education isn't necessary to become a data engineer. Nevertheless, getting the right kind of degree will help. For a data engineer, a bachelor's degree in engineering, computer science, physics, or applied mathematics is sufficient. However, you might want to study for a master's degree in computer engineering or computer science. It will help you compete against other job applicants—even if you don't have prior work experience as a data engineer.

Some of the best data engineers are self-taught via free and inexpensive online-learning programs. Believe it or not, you could probably learn most of what you need to know by watching videos on YouTube. They have an entire track dedicated to teaching data engineering.

As you get deeper into your learning, you'll need to master a variety of coding languages, operating systems, and information systems.

3) Project-Based Learning

Finding the motivation to complete online data engineering coursework can be difficult. Many would-be data scientists quit before getting their feet wet. If that happens to you, consider the project-based learning approach:

Pick a project that sounds interesting to you. Learn the skills that you need to go along with completing the project. Project-based learning can be more fun and practical way of learning data engineering.

To add a lot more fuel to the project-based learning approach, consider writing about your work and research. Open a Medium account and devote some time to creating a few "how-to" articles on the topic of data engineering. You could also post your personal projects to Github and contribute to open projects there on Github. Doing so will boost your data engineering street cred to potential employers.

There are many professional certification courses for data science and data engineering. Here is a list of the most popular certificate courses in data engineering:

• Vendor-Specific Certifications: Oracle, Microsoft, IBM, Cloudera, and many other data science technology companies provide training for valuable certifications in their products.

• Certified Data Management Professional (CDMP): Data Management Association International (DAMA) developed the CDMP program as a credential for being a general database professional.

• Cloudera Certified Professional (CCP) Data Engineer: The Cloudera CCP designation is a certification for professional data engineers. It covers topics like data transformations, staging and storing information, data ingestions, and a lot more. 

• Google Cloud Certified Professional Data Engineer: Applicants can receive the Google Cloud data engineer certification after successfully passing a two-hour exam.

However, these courses may not be as valuable as you think. Data engineering is something you learn by doing. Companies hiring data engineers know this. 

If your employer is sponsoring you to get one of these certifications, that’s great. If you're studying on your own, though, remember that learning by doing is infinitely more valuable than a certification.

Read more: Too Many Data Engineers? How to Get the Right Balance

How Integrate.io Helps Data Engineers

As you move forward in this field, you'll discover how important data warehousing integration tools are to your job. You'll also learn that not all of these tools are the same. Compared to others, some are vastly easier to use and more powerful, like Integrate.io.

Integrate.io is a new data warehousing integration solution built for e-commerce that allows you to create visual data pipelines within minutes using the following methods: 

• Extract, Transform, Load (ETL): You can extract e-commerce data, transform it into the correct format for analytics, and load it to a data warehouse for analysis. From here, you can run that data through BI tools and generate insights about e-commerce processes such as sales, marketing, customer service, and inventory management. Integrate.io can extract data from relational databases, transactional databases, SaaS tools, social media platforms, customer relationship management (CRM) systems, and other e-commerce platforms.

• Extract, Load, Transform (ELT): You can extract e-commerce data, load it into a warehouse, and transform it into the right format for analytics. ELT is a data integration method best suited for unstructured data sets. 

• Reverse ETL: You can move e-commerce data from a warehouse to an operational system. This method helps you analyze data in a tool you already use in your organization. 

• Super-Fast Change Data Capture (CDC): You can sync one or more e-commerce databases and track any changes made to those databases. 

Integrate.io’s pre-built out-of-the-box connectors make it easy to move data between sources and target systems with little or no code. However, data engineers can also create custom data pipelines based on the requirements of their e-commerce organization. Other Integrate.io benefits include Salesforce-to-Salesforce integration, excellent customer service, and a simple pricing structure. 

Integrate.io helps you move data between e-commerce sources and destinations with its out-of-the-box connectors and drag-and-drop point-and-click interface. Create your data integration workflows based on the needs of your e-commerce enterprise. Schedule an intro call to learn more.