With the influx of huge amounts of data from a multitude of sources, data engineering has become essential to the data ecosystem and organizations are looking to build and expand their team of data engineers.
If you’re looking to pursue a career in data engineering, this guide is for you to learn more about data engineering and the role of a data engineer and gain familiarity with the essential data engineering concepts.
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts. Fields like machine learning and deep learning can’t succeed without data engineers to process and channel that data.
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.
Data engineers focus on collecting and preparing data for use by data scientists and analysts. They take on three main roles as follows:
Data engineers with a general focus typically work on small teams, doing end-to-end data collection, intake and processing. They may have more skill than most data engineers, but less knowledge of systems architecture. A data scientist looking to become a data engineer would fit well into the generalist role.
A project a generalist data engineer might undertake for a small, metro-area food delivery service would be to create a dashboard that displays the number of deliveries made each day for the past month and forecasts the delivery volume for the following month.
These data engineers typically work on a midsize data analytics team and more complicated data science projects across distributed systems. Midsize and large companies are more likely to need this role.
A regional food delivery company might undertake a pipeline-centric project to create a tool for data scientists and analysts to search metadata for information about deliveries. They might look at distance driven and drive time required for deliveries in the past month, then use that data in a predictive algorithm to see what it means for the company's future business.
These data engineers are tasked with implementing, maintaining and populating analytics databases. This role typically exists at larger companies where data is distributed across several databases. The engineers work with pipelines, tune databases for efficient analysis and create table schemas using extract, transform, load (ETL) methods. ETL is a process in which data is copied from several sources into a single destination system.
- Extracting and integrating data from a variety of sources—data collection.
- Preparing the data for analysis: processing the data by applying suitable transformations to prepare the data for analysis and other downstream tasks. Includes cleaning, validating, and transforming data.
- Designing, building, and maintaining data pipelines that encompass the flow of data from source to destination.
- Design and maintain infrastructure for data collection, processing, and storage—infrastructure management.
As mentioned, we have incoming data from all resources across the spectrum: from relational databases and web scraping to news feeds and user chats. The data coming from these sources can be classified into one of the three broad categories:
- Structured data
- Semi-structured data
- Unstructured data
It has a well-defined schema.
Examples include data in relational databases, spreadsheets etc.
It has some structure but no rigid schema and typically has metadata tags that provide additional information.
Examples include JSON and XML data, emails, zip files, and more
It lacks a well-defined schema.
Examples include images, videos and other multimedia files, website data.
As the data engineer job has gained more traction, companies such as IBM and Hadoop vendor Cloudera Inc. have begun offering certifications for data engineering professionals. Some popular data engineer certifications include the following:
- Certified Data Professional is offered by the Institute for Certification of Computing Professionals, or ICCP, as part of its general database professional program. Several tracks are offered. Candidates must be members of the ICCP and pay an annual membership fee to take the exam.
- Cloudera Certified Professional Data Engineer verifies a candidate's ability to ingest, transform, store, and analyze data in Cloudera's data tool environment. Cloudera charges a fee for its four-hour test. It consists of five to 10 hands-on tasks, and candidates must get a minimum score of 70% to pass. There are no prerequisites, but candidates should have extensive experience.
- Google Cloud Professional Data Engineer tests an individual's ability to use machine learning models, ensure data quality, and build and design data processing systems. Google charges a fee for the two-hour, multiple choice exam. There are no prerequisites, but Google recommends having some experience with Google Cloud Platform.
As with many IT certifications, those in data engineering are often based on a specific vendor's product, and the trainings and exams focus on teaching people to use their software.
Certifications alone aren't enough to land a data engineering job. Experience is also necessary to be considered for a position. Other ways to break into data engineering include the following:
- University degrees. Useful degrees for aspiring data engineers include bachelor's degrees in applied mathematics, computer science, physics or engineering. Also, master's degrees in computer science or computer engineering can help candidates set themselves apart.
- Online courses. Inexpensive and free online courses are a good way to learn data engineering skills. There are many useful videos on YouTube, as well as free online courses and resources, such as the following six options:
a. Codecademy's Learn Python. Knowledge of Python is essential for data engineers. This course requires no prior knowledge.
b. Coursera's guide to Linux server management and security. This four-week course covers the Linux basics.
c. GitHub SQL Cheatsheet. This GitHub repository is consistently updated with SQL query examples.
d. O'Reilly data engineering e-books. Titles in the big data architecture section cover data engineering topics.
e. Udacity Data Engineering Nanodegree. Udacity's online learning offerings include a data engineering track.
3.Project-based learning. With this more practical approach to learning data engineering skills, the first step is to set a project goal and then determine which skills are necessary to reach it. The project-based approach is a good way to maintain motivation and structure learning.
4.Develop your communication skills.
Last but not least, data engineers also need communication skills to work across departments and understand the needs of data analysts and data scientists as well as business leaders. Depending on the organization, data engineers may also need to know how to develop dashboards, reports, and other visualizations to communicate with stakeholders.
Data engineers require a significant set of technical skills to address their highly complex tasks. However, it’s very difficult to make a detailed and comprehensive list of skills and knowledge to succeed in any data engineering role; in the end, the data science ecosystem is rapidly evolving, and new technologies and systems are constantly appearing. This means that data engineers must be constantly learning to keep pace with technological breakthroughs.
Notwithstanding this, here is a non-exhaustive list of skills you’ll need to develop to become a data engineer:
The raw data collected from various sources should be staged in a suitable repository. You should already be familiar with databases—both relational and non-relational. But there are other data repositories, too.
Before we go over them, it'll help to learn about two data processing systems, namely, OLTP and OLAP systems:
Are used to store day-to-day operational data for applications such as inventory management. OLTP systems include relational databases that store data that can be used for analysis and deriving business insights.
Are used to store large volumes of historical data for carrying out complex analytics. In addition to databases, OLAP systems also include data warehouses and data lakes (more on this shortly).
The source and type of data often determine the choice of data repository.
A data warehouse refers to a single comprehensive store house of incoming data.
Data lakes allow to store all data types—including semi-structured and unstructured data—in their raw format without processing them. Data lakes are often the destination for ELT processes (which we’ll discuss shortly).
You can think of data mart as a smaller subsection of a data warehouse—tailored for a specific business use case common
Recently, data lake houses are also becoming popular as they allow the flexibility of data lakes while offering the structure and organization of data warehouses.
Data pipelines encompass the journey of data—from source to the destination systems—through ETL and ELT processes.
They includes the following steps:
- Extract data from various sources
- Transform the data—clean, validate, and standardize data
- Load the data into a data repository or a destination application
- ETL processes often have a data warehouse as the destination.
A variation of the ETL process where instead of extract, transform, and load, the steps are in the order: extract, load, and transform meaning the raw data collected from the source is loaded to the data repository—before any transformation is applied. This allows us to apply transformations specific to a particular application. ELT processes have data lakes as their destination.
Data engineers must also understand NoSQL databases and Apache Spark systems, which are becoming common components of data workflows. Data engineers should have a knowledge of relational database systems as well, such as MySQL and PostgreSQL. Another focus is Lambda architecture, which supports unified data pipelines for batch and real-time processing.
Business intelligence (BI) platforms and the ability to configure them are another important focus for data engineers. With BI platforms, they can establish connections among data warehouses, data lakes and other data sources. Engineers must know how to work with the interactive dashboards BI platforms use.
Although machine learning is more in the data scientist's or the machine learning engineer's skill set, data engineers must understand it, as well, to be able to prepare data for machine learning platforms. They should know how to deploy machine learning algorithms and gain insights from them.
knowledge of Unix-based operating systems (OS) is important. Unix, Solaris and Linux provide functionality and root access that other OSes -- such as Mac OS and Windows -- don't. They give the user more control over the OS, which is useful for data engineers.
The list of tools data engineers should know can be overwhelming.
But don’t worry, you do not need to be an expert at all of them to land a job as a data engineer. Before we go ahead with listing the various tools data engineers should know, it’s important to note that data engineering requires a broad set of foundational skills including the following:
Programming language: Intermediate to advanced proficiency in a programming language preferably one of Python, Scalar, and Java
Databases and SQL: Good understanding of database design and ability to work with databases both relational databases such as MySQL and PostgreSQL and non-relational databases such as MongoDB.
Command-line fundamentals: Familiarity with Shell scripting and data processing and the command line.
Knowledge of operating systems and networking.
Data warehousing fundamentals
Fundamentals of distributed systems
Even as you are learning the fundamental skills, be sure to build projects that demonstrate your proficiency. There’s nothing as effective as learning, applying what you’ve learned in a project, and learning more as you work on it!
In addition, data engineering also requires strong software engineering skills including version control, logging, and application monitoring. You should also know how you use containerization tools like Docker and container orchestration tools like Kubernetes.
Though the actual tools you use may vary depending on your organization, it's helpful to learn:
- dbt (data build tool) for analytics engineering
- Apache Spark for big data analysis and distributed data processing
- Airflow for data pipeline orchestration
- Fundamentals of cloud computing and working with at least one cloud provider such as AWS or Microsoft Azure.
The next step to becoming a data engineer is to work on some projects that will demonstrate your skills and understanding of core subjects. You can check out our full guide on building a data science portfolio for some inspiration.
You’ll want to demonstrate the skills we’ve already outlined in order to impress potential employers, which means working on a variety of different projects. DataCamp Workspace provides a collaborative cloud-based notebook that allows you to work on your own projects, meaning you can analyze data, collaborate with others, and share insights.
You can also apply your knowledge to various data science projects, allowing you to solve real-world problems from your browser, while also contributing to your date engineering portfolio.
When you feel that you are ready to explore a specific business area of your choice, you may start focusing on gaining domain knowledge and making individual projects related to that particular sphere.
Data engineering is one of the most in-demand positions in the data science industry. From Silicon Valley big tech to small data-drive startups across sectors, businesses are looking to hire data engineers to help them scale and make the most of their data resources. At the same time, companies are having trouble finding the right candidates, given the broad and highly specialized skill set required to meet an organization's needs.
Given this particular context, there is no perfect formula to land your first data engineering job. In many cases, data engineers arrive in their position following a transition from other data science roles within the same company, such as data scientist or database administrator.
Instead, if you are looking for data engineering opportunities in job portals, an important thing to keep in mind is that there are many job openings that include to the title “data engineer”, including cloud data engineer, big data engineer, and data architect. The specific skills and requirements will vary from position to position, so the key is to find a closer match between what you know and what the company needs.
The answer is simple: keep learning. There are many pathways to deepen your expertise and broaden your data engineering toolkit. You may want to consider a specialized and flexible program for data science, such as our Data Engineer with Python track.
You could also opt for further formal education, whether it’s a bachelor’s degree in data science or computer science, a closely related field, or a master’s degree in data engineering.
In addition to education, practice is the key to success. Employers in the field are looking for candidates with unique skills and a strong command of software and programming languages. The more you train your coding skills in personal projects and try big data tools and frameworks, the more chances you will have to stand out in the application process. To prove your expertise, a good option is to get certified in data engineering.
Finally, if you are having difficulties finding your first job as a data engineer, consider applying for other entry-level data science positions. In the end, data science is a collaborative field with many topics and skills that are transversal across data roles. These positions will provide you with valuable insights and experience that will help you land your dream data engineering position.
Data engineering interviews are normally broken down into technical and non-technical parts;
Recruiters will want to know your experiences that are related to the data engineering position. Make sure to highlight your previous work in data science positions and projects in your resume and prepare to provide full detail about them, as this information is critical for recruiters to assess your technical skills, as well as your problem-solving, communication, and project management.
This is probably the most stressful part of a data science interview. Generally, you will be asked to resolve a problem in a few lines of code within a short time, using Python or a data framework like Spark.
You will not go far in your data engineering career without solid expertise in SQL. That’s why, in addition to the programming test, you may be asked to solve a problem that involves using SQL. Typically, the exercise will consist of writing efficient queries to do some data processing in databases.
This is the most conceptual part of the technical interview and probably the most difficult. Designing data architectures is one of the most impactful tasks of data engineers. In this part, you will be asked to design a data solution from end to end, which normally comprises three aspects: data storage, data processing, and data modeling.
Once you have completed the technical part, the last step of the data engineering interview will consist of a personal interview with one or more of your prospective team members. The goal? To discover who you are and how you would fit in the team.
But remember, the data engineer interview is a two-sided conversation, meaning that you should also pose questions to them to determine whether you could see yourself as a part of the team.
Data engineering is an emerging job, and it’s not always easy for recruiters to find the right candidates. Competition for this difficult-to-find talent is high among companies, and that translates into some of the highest salaries among data science roles.
Data engineering is one of the most in-demand jobs in the data science landscape and is certainly a great career choice for aspiring data professionals. If you are determined to become a data engineer but don’t know how to get started, we highly recommend you follow our career track Data Engineer with Python, which will give you the solid and practical knowledge you’ll need to become a data engineering expert.