For data engineering positions, big data skills are essential. Professionals in data engineering are responsible for a wide range of responsibilities, including the creation, construction, and upkeep of data pipelines, the collection of raw data from various sources, and performance optimization. Big data frameworks, databases, building data infrastructure, containers, and other topics are expected of them. Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, and Pig, to name just a few, must also be taught to them in the real world.
A data engineer is an essential position in any company that deals with a lot of data. As specialists in information technology, data engineers frequently possess expertise in a wide range of processes and applications. By mastering and honing these skills, you can become a more qualified candidate and an effective data engineer.
In this article, we make sense of the job of data engineers, investigate probably the main abilities for this calling and rundown the means important to begin a profession in data engineering.
What do data engineers do?
Data engineers create and keep up with the design utilized in different information science projects. They are accountable for ensuring that data flows uninterruptedly between applications and servers.
Data engineering consolidates components of computer programming and information science. A data engineer’s primary responsibilities include streamlining the existing foundational processes for data collection and use, integrating new software and data management technologies into an existing system, and developing data collection processes.
30 must have data engineer skills
Data engineers frequently possess the following technical and soft skills in order to carry out their responsibilities effectively and efficiently:
- Coding
- Data warehousing
- Knowledge of operating systems
- Database systems
- Data analysis
- Critical thinking skills
- Basic understanding of machine learning
- Communication skills
- SQL
- Data architecture
- Apache hadoop-based analytics
- Data transformation tools
- Data ingestion tools
- Data mining tools
- Real-time processing frameworks
- Data buffering tools
- Cloud computing
- Data visualization skills
- Data modeling techniques
- Python skills
- AWS cloud services skills
- Kafka
- NoSQL
- Scripting
- Data pipelines
- Hyper automation
- Apache airflow
- Apache spark
- ELK stack
- Amazon redshift
1. Coding
The majority of data engineering positions require coding skills, which are highly valued. A lot of employers want candidates to know at least the fundamentals of languages like:
- Python
- Golang
- Ruby
- Perl
- Scala
- Java
- SAS
- R
- MatLab
- C and C++
2. Data warehousing
An enormous amount of data must be stored and analyzed by data engineers. In a data engineering position, therefore, familiarity and experience with data warehousing solutions like Redshift or Panoply are essential. Those with experience managing and analyzing data from data warehouses may be able to find more roles for which they are qualified due to the growing use of data warehouses.
ETL and data warehouses aid businesses in making effective use of big data. It makes data from different sources easier to understand. ETL, or Extract Transform Load, loads data into the warehouse after converting it for analysis from multiple sources. Talend, Informatica PowerCenter, AWS Glue, Stitch, and other popular ETL tools are among them.
3. Knowledge of operating systems
It is essential for a data engineer to have a thorough understanding of operating systems like Linux, Solaris, UNIX, and Apple macOS. Understanding the intricacies of various devices and operating systems can help you succeed in this industry because they each offer distinct advantages and can satisfy distinct requirements.
In particular, the Linux operating system may be used by data engineers to handle large amounts of unstructured data, whereas Windows may be used by them to manage server clusters.
4. Database systems
Database administration should be thoroughly understood by data engineers. Having a thorough understanding of Structured Query Language (SQL), which is considered to be the most widely used solution, is extremely beneficial in this field. SQL is a coding language for databases that manages and extracts data from tables. If you want to work as a freelance data engineer, you should also learn about Bigtable and Cassandra, two other database solutions.
A thorough comprehension of database design and architecture is essential for data engineering positions that require storing, organizing, and managing large volumes of data. Structure query language (SQL)-based and NoSQL-based databases are the two most frequently utilized kinds of databases.
NoSQL technologies, such as Cassandra, MongoDB, and others, can store large volumes of structured, semi-structured, and unstructured data in accordance with application requirements, whereas SQL-based databases like MySQL and PL/SQL are used to store structured data.
5. Data analysis
Candidates for the position of data engineer are typically expected to have a solid understanding of analytics software, particularly Apache Hadoop-based solutions such as MapReduce, Hive, Pig, and HBase. Building systems that gather data for use by other analysts or scientists is a primary focus for engineers. You can create and improve such systems with the help of strong analytical skills you possess yourself.
6. Critical thinking skills
Data engineers look at problems and come up with creative and efficient solutions. Since there are periodically when you should foster an answer that doesn’t as yet exist, the capacity to think fundamentally is vital. When designing and troubleshooting data collection and management systems, critical thinking is also used to find effective solutions to problems.
7. Basic understanding of machine learning
However, AI is essentially the focal point of information researchers, it tends to be useful for data engineers to have basically a fundamental comprehension of utilizing this kind of information. You can distinguish yourself as an incredible asset to any organization by developing your knowledge of data modeling and statistical analysis, which can assist you in developing solutions that are usable by peers.
AI and its application to computerized reasoning is likewise an immeasurably growing field across a wide scope of enterprises, so finding out about and understanding it can make data engineers more ready to apply their abilities to more professional valuable open doors.
8. Communication skills
As a data engineer, you team up with partners with and without specialized mastery, which is the reason having incredible relational abilities is significant. You may share your findings and suggestions with peers who do not have a technical background, despite the fact that you frequently collaborate with data experts like data scientists and data architects.
With the rise of remote work in modern businesses, strong digital communication skills in text, video, and audio formats are also becoming increasingly important.
9. SQL
For data engineers, SQL is the fundamental set of skills. SQL is a prerequisite for managing an RDBMS (relational database management system). You will need to answer a long list of questions in order to accomplish this. Memorizing a query is only one part of learning SQL. Optimized query writing is a skill that must be acquired.
10. Data architecture
Data engineers should have the necessary information and knowledge to assemble complex data set frameworks for organizations. It is connected to the operations that are used to deal with data in motion, data at rest, datasets, and the connection between processes and applications that depend on data.
11. Apache hadoop-based analytics
Datasets are used to compute distributed processing and storage using the open-source platform known as Apache Hadoop. They help with data processing, access, storage, governance, security, and operations in a wide range of ways. You can expand your skill sets with Hadoop, HBase, and MapReduce.
12. Data transformation tools
Big data exists in raw form and cannot be utilized directly. Depending on the use case, it needs to be converted into a format that can be consumed. Depending on the data sources, formats, and desired output, data transformation can be simple or complex.
A portion of the information change instruments are Hevo Information, Matillion, Talend, Pentaho Information Joining, InfoSphere DataStage, and that’s only the tip of the iceberg.
13. Data ingestion tools
Data ingestion is one of the fundamental pieces of enormous information and data abilities and is the most common way of moving information from at least one source to an objective where it very well may be dissected.
Professionals must be familiar with data ingestion tools and APIs in order to prioritize data sources, validate them, and dispatch data in order to ensure an efficient ingestion process as the amount and formats of data increase. A portion of the data ingestion devices to know are Apache Kafka, Apache Tempest, Apache Flume, Apache Sqoop, Wavefront, and that’s only the tip of the iceberg.
14. Data mining tools
Data mining, which entails extracting essential information from large data sets in order to identify patterns and prepare them for analysis, is another essential skill for handling big data. Classification and prediction of data are made easier with the aid of data mining. Big data professionals must be able to use Apache Mahout, KNIME, Rapid Miner, Weka, and other data mining tools.
15. Real-time processing frameworks
Real-time data processing is necessary to quickly generate actionable insights. Apache Flash is most prevalently utilized as a conveyed constant handling structure to convey information handling. Hadoop, Apache Storm, Flink, and others are additional frameworks to be familiar with.
16. Data buffering tools
With expanding information data volumes, data buffering has turned into a vital driver to accelerate the handling force of information. A data buffer is essentially a location that temporarily stores data while it moves between locations.
When thousands of data sources continuously produce streaming data, data buffering becomes crucial. Data buffering tools like Kinesis, Redis Cache, GCP Pub/Sub, and others are commonly used.
17. Cloud computing skills
One of the most important tasks for big data teams is setting up the cloud to store data and make sure it is always available. It, in this manner, turns into a fundamental expertise to procure while working with large information.
Depending on the requirements for data storage, businesses utilize hybrid, public, or in-house cloud infrastructure. AWS, Azure, GCP, OpenStack, Openshift, and other popular cloud platforms are among the ones you should be aware of.
18. Data visualization skills
Professionals in big data work extensively with visualization tools. The generated insights and lessons must be presented in a format that can be used by end users. A portion of the famously utilized perception devices that can be learned are Scene, Qlik, Tibco Spotfire, Plotly, and the sky is the limit from there.
19. Data modeling techniques
Understanding how to design databases and warehouses in a way that is efficient and scalable is necessary for data modeling. Using data modeling techniques to carry out data pipelines is a crucial part of data engineering, making this a necessary skill.
Data modeling can be started with Power BI tools, and our course Data Modeling in Power BI is the best way to learn more about it.
20. Python skills
Python is often regarded as one of the most widely used programming languages. Data pipelines, integrations, automation, and data cleaning and analysis are all possible with it. Additionally, it is one of the best languages to begin with and one of the most adaptable.
Python is so common that it is used in the back end of many data engineering tools and often allows for integration with data engineering tasks. Check out our Data Engineer with Python course if you want to learn Python for the first time. It will teach you how to create an efficient data architecture, simplify data processing, and maintain large-scale data systems.
21. AWS cloud services skills
Redshift, EC2, and other services make up the AWS cloud service. Over the years, the use of cloud-based services has grown significantly, and AWS is the most widely used platform to get started. Data engineers need distributed computing abilities, and you can begin fostering yours with our AWS Cloud Ideas course.
22. Kafka
Kafka is a real-time data feed management platform for open-source processing software. Businesses require real-time streaming apps, which means you can use it to build them. Apps based on Kafka can help find trends, apply them, and almost immediately respond to customer needs.
Because of this, Kafka is used by 60% of the Fortune 100 companies in their applications. LinkedIn, Microsoft, Netflix, Airbnb, and Target are a few examples. For instance, The New York Times makes use of Kafka to store and distribute published content to apps so that readers can access it.
23. NoSQL
This is an alternate kind of conveyed information capacity that is turning out to be progressively well known. Simply put, “NoSQL” refers to technology based on something other than SQL.
Apache River, BaseX, Ignite, Hazelcast, Coherence, and numerous others are all examples of NoSQL. Knowing how to use them would be extremely helpful in your job search as a data engineer because you will most likely come across them.
24. Scripting
Yes, scripting skills as data engineers are still required. Linux Bash, PowerShell, Typescript, JavaScript, and Python are still around, and we’re dealing with even more data types (text-based support for CSV, TSV, JSON, Avro, Parquet, XML, and ORC, among others). in the data pipeline that requires more ETL/ELT tools and techniques knowledge.
25. Data pipelines
Implementations of desperate data lakes continue to receive new names, such as DataBricks Lakehouse and Snowflakes Data Cloud, which are the same thing as the new year. Working with raw data in the form of JSON, CSV, and real-time streams is commonplace.
The way and where information engineers set up capacity might change information engineer ranges of abilities and instruments that are expected for the ETL/ELT infusion. This is one region that is getting more complicated and slanted relying upon the source and asset utilized.
26. Hyper automation
Running jobs, schedules, and events are examples of value-added tasks that are now part of a data engineer’s skill set. The most recent 10 years shows this pattern getting more prevalent with particular Prearranging and Data Pipelines errands expected to effectively move information to the cloud.
“The most successful hyper-automation teams focus on three key priorities,” according to Gartner. boosting decision-making agility, speeding up business processes, and improving work quality.
27. Apache airflow
Work automation is one of the quickest ways to achieve functional efficiency and plays a critical role in every industry. We need Apache Airflow to automate some tasks so that we don’t get stuck doing the same things over and over again.
The majority of data engineers are responsible for managing a variety of workflows, such as uploading, pre-processing, and collecting data from multiple databases. As a result, it would be wonderful if our daily tasks could simply trigger automatically at a predetermined time and all processes could be carried out in the correct order.
One such tool that could be very useful for you is Apache Airflow. This tool will definitely be useful to you, regardless of whether you are a software engineer, data scientist, or data engineer.
28. Apache spark
It is the best data handling structure in ventures today. It’s true that Spark is expensive because it uses a lot of RAM for in-memory computation, but Data Scientists and Big Data Engineers still love it.
Associations that regularly depended on Guide Lessen like systems are currently moving to the Apache Flash structure. Spark is 100 times faster than Map Reduce frameworks like Hadoop and does in-memory computing.
It supports R, Python, Java, and Scala, among other languages. Additionally, it provides a framework for processing graph, streaming, and structured data. Big data can also be used to train machine learning models and build ML pipelines.
29. ELK stack
Elasticsearch, Logstash, and Kibana are the three open-source products that make up this amazing collection.
ElasticSearch: Another kind of NoSQL database is this one. You can store, search, and analyze a lot of data with it. ElasticSearch is the best option for your technology stack if full-text search is a part of your use case. It even permits fuzzy matching searches.
Logstash: It is a tool for pipeline data collection. It can gather information from pretty much every asset and makes it accessible for additional utilization.
Kibana: It is an information perception device that can be utilized to imagine the elasticsearch reports in various graphs, tables, and guides.
Slack, Udemy, Medium, and Stackoverflow are among the more than 3,000 businesses utilizing the ELK stack in their technology stacks. You can start learning ELK Stack from the free resources listed here.
30. Amazon redshift
Amazon’s cloud computing platform is AWS. It has the biggest piece of the pie of any cloud stage. Redshift is an information stockroom framework, a social data set intended for question and examination. Redshift makes it simple to query petabytes of structured and semi-structured data.
For Fortune 500 companies, startups, and everything in between, Redshift powers analytical workloads. Redshift is a requirement for the majority of data engineering job descriptions.
How to become a data engineer?
To become a data engineer, you must follow the following steps:
- Earn your bachelor’s degree
- Develop your skills
- Pursue certifications
- Gain experience
- Consider a master’s degree
1. Earn your bachelor’s degree
The majority of employers require data engineers to have at least a bachelor’s degree, despite the fact that there are many other factors that are just as important as formal education when entering this field.
You might consider chasing after a degree in data innovation, software engineering, PC designing, programming, applied math, measurements, material science or a connected field. Prioritize taking courses in coding, database management, algorithms, or data structures if you decide to pursue a degree outside of one of these majors.
2. Develop your skills
You can also take on personal projects that allow you to grow your expertise in the field and develop your expertise with important solutions and programming languages, such as Python and SQL, in addition to internships, which are frequently a great way to expand your skill set and gain valuable experience.
Ensure that you integrate these encounters into your portfolio so you can show future businesses what you’re prepared to do.
3. Pursue certifications
Certifications in data engineering are extremely valuable and a great way to show off your skills. A portion of the top choices include:
- CCP Data Engineer from Cloudera
- IBM Certified Data Science Professional
- Google Certified Professional
CCP Data Engineer from Cloudera:
Cloudera solutions are specifically covered by this certification. It’s a great way to demonstrate to potential employers that you’ve worked with ETL analytics and tools before.
IBM Certified Data Science Professional:
In this sector, the IBM Certified Data Science Professional certification is a well-liked option. It focuses on developing big data application skills.
Google Certified Professional:
With this confirmation, you show managers that you knew all about the essential standards of information designing and can fill a situation as either an expert or a partner in the field.
4. Gain experience
Even though you might prefer an entry-level position in data engineering, any IT-related position can give you a lot of experience and show you how to deal with problems in data organization.
Beside permitting you to foster your decisive reasoning and critical thinking abilities, a passage level occupation permits you to comprehend the various parts of this industry, how it capabilities and exactly the way in which cooperative it is. For instance, data engineers collaborate with management, data scientists, and data architects to collect, analyze, and make use of data.
5. Consider a master’s degree
Even though getting an advanced degree is rarely required, it is a great way to learn more, improve your skills, and advance your career. You can become a more competitive candidate for the position of data engineer by earning a master’s degree in computer science or computer engineering.
You could also become an expert in a particular kind of data analysis or machine learning, which could be a great way to show how valuable you are over time.
Why pursue a career in data engineering?
Data science was named the sexiest job of the 21st century almost a decade ago. This set off a fire under a field that was already expanding, and data scientists began to flood the job market.
However, big tech companies like Facebook and AirBnB quickly realized that in addition to the demand for analytics and predictive modeling, they also needed the right people and tools to collect, store, manage, and transform their data so that it is highly accessible when it reaches their data scientists. Enter: the engineer of data
In the past few years, data engineering has grown significantly. The most recent growth period, from 2021 to 2022, saw a 100 percent increase in data engineering, exceeding that of the data scientist. In addition, when compared to other tech roles, it has the fourth highest volume of job postings. This demonstrates the current job market’s high demand for data engineers.
The fact of the matter is that there will always be a need for data engineers as long as data is used in a business to guide decision-making or provide answers to business questions. Therefore, there has never been a better time to pursue a career in data engineering.
Hey, I am Sachin Ramdurg. I run and manage futuredecider.com website that helps students, graduates, and professionals, to find and decide on their future career with ultimate future career advices and future career guides. I have an overall 12+ years of career guidance experience in multiple domains which has helped multiple students, graduates, and professionals to find the best career path for their future.