Discover more about the core data engineer skills and how to become a data engineer with this guide from Coursera. Data engineering is in high demand. Learn the skills so that you can build the data engineer competencies required in today’s job market.
Data engineering is a profession with skills that are positioned between software engineering and programming on one side, and advanced analytics skills like those needed by data scientists on the other side.
To be successful in data engineering requires solid programming skills, statistics knowledge, analytical skills, and an understanding of big data technologies. This guide can help you to understand the skills you will need to acquire and how to begin on this exciting career path.
Data engineers are responsible for designing and managing infrastructure that allows easy access to all types of data (structured and unstructured). As a data engineer, you will be responsible for designing, constructing, installing, testing, and maintaining architectures, including databases and systems for large-scale processing. You will also develop, maintain, and test data management systems.
Data engineers use their technical expertise to ensure the systems they build are secure, scalable, and reliable—meaning they can handle vast amounts of data and provide it in real-time. Data engineering is a rapidly growing field with many lucrative job opportunities.
The explosive growth in the amount of data, the wide variety of data types, and the computing power required to make sense of it are fueling demand for people who can design systems for collecting and analyzing all this information. Data engineers are in high demand across a wide range of industries, from health care to e-commerce to finance to technology.
So what do data engineer job postings indicate are essential application criteria? The requirements for a career in data engineering vary between employers. However, there are some data engineer competencies that you’ll see consistently in data engineer job listings. These include:
Knowledge of distributed systems like Hadoop and Spark as well as cloud computing platforms such as Azure and AWS
Strong programming skills in at least one programming language like Java, Python, or Scala
Good knowledge of relational databases or NoSQL databases like MongoDB or Cassandra
Strong understanding of machine learning principles, statistics, algorithms, and math concepts
As a data engineer, you’ll need to feel comfortable with various data-related programs and languages. Some are mandatory, and others are simply nice to have. Here are some of the most common ones:
Apache Hadoop and Apache Spark
Amazon Web Services/ Redshift (for data warehousing)
HDFS and Amazon S3
To become a data engineer, you should be familiar with some of the most popular data science programs. Some details about the more important programs are listed below.
These are open-source, Java-based frameworks that allow for the distributed processing of large data sets across clusters of computers.
Hadoop is a framework for distributed applications that solves the challenges of dealing with large amounts of data. It is helpful for addressing computationally difficult problems and can be used for batch processing, iterative algorithms, and interactive queries.
Spark is a fast, in-memory data processing engine with elegant APIs in Scala, Java, and Python. It uses Hadoop clusters through Spark or YARN's standalone mode, and it can data-process in Hive, HDFS, Cassandra, HBase, and any Hadoop InputFormat.
C++ is a general-purpose programming language that emerged from the B programming language, developed at Bell Labs. Created by Bjarne Stroustrup as an enhancement to C, it has evolved into a language with object-oriented capabilities and is also used to build sophisticated web applications.
Used to provide database warehousing solutions, Amazon Web Services/Redshift is a cloud computing platform that works along with Amazon S3 buckets and Amazon EC2 instances to store your data.
Microsoft has made a big move into the cloud space with its Azure platform. It includes tools for storage, computing, analytics, and more.
HDFS and Amazon S3: These are two of the most popular cloud-based data storage solutions today. HDFS is an open-source file system built to store large amounts of data in commodity hardware. Amazon S3 is a scalable object storage system that can store one or more terabytes of data per file in a highly redundant manner.
There is a wide range of skills you'll need to acquire to be a technically-savvy data engineer. The list below details just some of the critical areas that you can expect to study in your role as a data engineer, but these may vary depending on the company you work for or the project you're working on.
Data engineers need to have an in-depth knowledge of various database systems (SQL and NoSQL) and data warehousing solutions. As a data engineer, you'll need to know how to extract data from multiple sources, transform them into useful information, load them into a usable format, and present the results to inform business decisions.
Most of your job as a data engineer will focus on building the infrastructure that helps your company store and access its data efficiently. Most companies use some kind of data warehousing solution to help them achieve this goal, so it’s essential to have experience working with them before entering the field.
You also need a strong understanding of ETL (extract, transfer, load) tools to integrate data from disparate sources, manage large volumes of both structured and unstructured data, and develop algorithms.
The majority of large companies today already use machine learning techniques in some shape or form. As a data engineer, you'll be responsible for building models that drive these machine learning applications.
Interacting with data APIs is an essential skill for any technical data engineer. These days, the majority of tools and platforms have restful APIs—and you'll need to be able to interact with these services to build solutions.
If you're working in Python, there's a good chance you'll use the requests library as a straightforward way to interact with APIs. However, it can be helpful to know how to consume APIs in other languages.
Technical data engineers often work on polyglot teams, especially in the big data space. The most common programming languages used by these teams are Python, Java, and Scala. To become a technical data engineer, you'll need expertise in at least one (or ideally all) of these languages.
Technical data engineers write code that runs on clusters of hundreds or thousands of machines, and, therefore, you need to understand basic concepts related to distributed systems. This includes knowing about coordination protocols, consensus algorithms, and message brokers.
You need to have a deep understanding of how the different algorithms work to select them appropriately, and the same applies to data structures. You need to choose a suitable data structure that fits your needs. Bad choices can lead to significant performance problems or even unexpected behavior in your systems.
Data engineers are critical members of any big data team. While all the technical skills are essential, non-technical skills such as communication, collaboration, and presentation are valued more than ever. These workplace skills help you work more effectively with others in technical and non-technical roles, which helps your company achieve its business goals.
Data engineers must be able to communicate with both technical and non-technical colleagues to understand their goals and needs. You must also explain complex processes in simple terms so stakeholders can understand you. This is especially important for explaining results or insights uncovered in your data engineering projects. Without clear communication processes, tools and discoveries can remain underutilized.
Collaboration is another critical workplace skill for data engineers. You must work well with teams of other data engineers, data scientists, or other subject matter experts (SMEs) to build out the infrastructure necessary to support a company's business goals. Knowing how to collaborate and facilitate communication between groups is vital to your success in this role.
Data engineers often need to present the results of their projects. This means they need to be able to explain technical concepts in layperson’s terms and make convincing arguments for why a team should take specific actions based on the results of their work.
In a constantly moving world, many things are uncertain. However, one thing that is certain is that companies wanting to be competitive need to collect and organize data and make sense of it. There are different names for data engineers, and there are different levels to the role. Here is an overview of some of the job titles a data engineer might have and their average salaries:
Data engineer: $114,434 
Big data engineer: $126,178 
Enterprise data engineer: $112,469 
Data platform engineer: $120,583 
Senior data engineer: $141,938 
Data warehouse (DW) engineer: $106,845 
ETL developer: $112,965 
Enterprise data architect: $171,867 
Your path to a job in data engineering varies depending on your background and experience. You will need a relevant degree, certificates and certifications, and demonstrable experience.
The most common, yet not always mandatory, educational requirement for becoming a data engineer is to acquire a bachelor's degree. While there are many options to choose from, most employers want to see that their potential candidate holds a bachelor's degree in computer science, software engineering, math, or related fields.
To be successful as a data engineer, you need to be proficient in programming languages such as Java, Python, or Scala. It would be wise to consider acquiring certifications or certificates to ensure your knowledge is up-to-date and relevant in the industry. Some of the certificates that can give you an edge over the competition include:
If you are considering data engineer certifications, then the following should be on your shortlist.
IBM Certified Solution Architect: Cloud Pak for Data v4.x Certification
Amazon Web Services (AWS) Certified Data Analytics – Specialty Certification
SAS Certified Big Data Professional Certification
Cloudera Data Platform Generalist Certification
Data Science Council of America (DASCA) Big Data Engineer Certification
Data Science Council of America (DASCA) Associate Big Data Engineer Certification
One of the best ways to boost your experience as a data engineer is by working on projects. Your work experience largely determines your value as a data engineer. In the interview, employers will likely look at what projects you have worked on and ask questions about them to determine if you have the skills they need.
Explore and pursue opportunities to build your portfolio. You are more likely to have the competencies you need to win a job as a data engineer if you have diverse project experience.
In a word, practice. One of the most effective ways to gain experience is to practice something. You can do this by making your own side projects that involve data processing and analysis.
It doesn't have to be anything on a large scale, but it's important that you have something you can show off to potential employers. Some examples include:
A personal website with a blog to demonstrate your ability to write documentation
A GitHub project where you contribute code to demonstrate your coding skill
An open-source data science project to demonstrate your capability to work with others
A web application that processes raw data into something useful, such as Kaggle
You should also work on open-source projects that solve "real world" data engineering problems. Here are a few examples:
Build ETL pipelines with Apache Airflow.
Store data in a scalable database like Amazon S3 or Google BigQuery.
Use Python Pandas to analyze data and create visualizations.
Use Python Pandas to prepare data for machine learning model training.
Use Spark MLlib to train machine learning models.
Automate moving data between systems using an API like RESTful API or GraphQL API.
If you are currently in another job role but enjoy data, you could make a transition to data engineer. Some of the jobs that most frequently lead to data engineering are:
Software engineers with a passion for SQL and data
Data analysts with a passion for programming
Web developers with a passion for databases and data-driven projects
College grads with some computer science coursework, knowledge, and experience may be able to apply for entry-level data engineering roles.
The world is awash in data—and it’s growing faster every day. Our society has become increasingly dependent on data to make crucial decisions, and the demand for data engineers continues to grow after a period of cooling in the job market.
Do you want to learn the skills that will allow you to work with massive data sets and build data-driven applications? You can build your knowledge and skills online by learning how to use Apache Spark, NoSQL databases, Hadoop, and other big data technologies.
Leading professionals in the field design the courses and programs on Coursera to give you exposure and the opportunity for hands-on experience in the industry. An excellent place to start could be the IBM Data Engineering Professional Certificate offered by IBM.
Launch your new career in Data Engineering. Master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data and Spark with hands-on job-ready skills.
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Relational Database Management Syste (RDBMS), ETL & Data Pipelines, NoSQL and Big Data, Apache Spark, SQL, Data Science, Database (DBMS), NoSQL, Python Programming, Data Analysis, Pandas, Numpy, Extraction, Transformation And Loading (ETL), Information Engineering, Web Scraping, Database (DB) Design, Database Architecture, Postgresql, MySQL, Relational Database Management System (RDBMS), Cloud Databases, Ipython, Shell Script, Bash (Unix Shell), Linux, Database Servers, Relational Database, Database Security, database administration, Apache Kafka, Apache Airflow, Data Pipelines, Data Warehousing, Cube and Rollup, Business Intelligence (BI), Star and Snowflake Schema, cognos analytics, Mongodb, Cloud Database, Cloudant, Cassandra, Apache Hadoop, SparkSQL, SparkML, Big Data, Relational Databases
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