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We analyze over 1000 job profiles on LinkedIn to understand the skills required to become data engineers. In this blog, we sum up all the major skills and certifications required to become a Data engineer in 2022.

Let’s dive right into in

Introduction

Data engineering helps to make data more usable and accessible to data users. To do so, data engineering must extract, modify and analyze data for each system. For example, data stored in related websites are treated like tables in a Microsoft Excel spreadsheet. Each table has many rows, and all rows have the same columns. A specific piece of information can be stored in multiple tables, such as in customer order.

In contrast, data stored on NoSQL websites such as MongoDB is treated as text, similar to Word documents. Each document is flexible and may have a different set of attributes. When inquiring about related websites, the data engineer uses SQL, while MongoDB has a very different proprietary language than SQL. Data engineering works with both types of systems, and many others, making it easier for data consumers to use all the data together without handling all the complexities of other technologies.

Working with each system involves an understanding of technology and data. Once data engineering has acquired and selected data for a specific task, it is easier for data users to use it. For these reasons, even simple business questions can require complex solutions.

As companies rely heavily on data, the value of data engineering continues to grow. Since 2017 Google has been searching for the word “data engineering” three times:

And in that time, job postings for this role have also increased more than 50%. Just in the past year, they’ve almost doubled. The latest DICE report for the 2020 Tech Job Report reported Data Engineer as the fastest-growing job in 2019, growing by 50% in 2019. The report also found that it took an average of 46 days to complete data engineering roles and predicted it was time to hire. Data Engineers are likely to increase by 2020 “as more companies compete to find the talent they need to hold their data infrastructure.”

By seeing this chart and analysis, most of us will be amazed about such a tremendous craze of data engineering and its job. Now let us look at some exciting topics, i.e., Why there is such mesmerizing upward trend in Data engineering.

Following are some remarkable reasons and noticeable points why there is such massive demand for Data engineer and their flow:-

• There is more data than ever before, and data is growing faster. 90% of the data available today was created two years ago.
• Data is crucial in companies and all additional business activities — marketing, finance, and other business areas, and they use data to become more innovative and efficient.
• The technology used for data extraction is very complex. Today, many companies create data on multiple systems and use a range of different technologies in their data, including Hadoop and NoSQL.
• Companies find many ways to benefit from data. They use data to understand the current state of the business, predict the future, set in examples for their customers, avoid threats, and create new types of products. Data engineering is the linchpin of all these activities.
• As the data becomes more complex, this role will continue to grow in importance. And as data demand grows, data engineering will become more critical.

Roles and responsibilities of Data engineers

The data engineer will use specialized information to design, develop, evaluate a unit, prepare, and implement a data engineering solution that transports, refines, and uploads data to a variety of systems. One will work with your clients and the various roles within IT to gain an understanding of the business environment, technical context, and strategic direction. This person will also be responsible for producing the documents as required; complying with safety and quality standards, and staying informed of emerging trends. supported groups include Applied engineering, Data Engineering, Decision Science, and Business Statistics.

After surveying over 1000 job profiles of Data engineers on Linkedin. These are some main points that we can conclude

• Have experience in creating robust and automated pipelines to ingest and process structured and unstructured data sources into analytical platforms using batch and streaming mechanisms leveraging cloud native toolset
• Be able to develop high performance data queries, stored procedures and/or functional code for data related batch jobs, application support and ETL needs
• Leverage the right tools for the right job to ideliver testable, maintainable, and modern data solutions
• Be comfortable with researching data questions, identify root causes, and interact closely with business users and technical resources on various data related decisions
• Understand how to profile code, queries, programming objects and optimize performance
• Aspire to be efficient, thorough and proactive Responsibilities and Duties
• Develops data pipelines to ingest, move, transform, and integrate data in a secure and performant manner while ensuring and enforcing general data governance principals.
• Explores new technologies and data processing methods to increase efficiency, performance, flexibility, and usefulness to the Enterprise’s data.
• Document requirements and translate into proper system requirements specifications using high maturity methods, processes and tools.
• Execute and coordinate requirements management and change management processes. Participates as a member of and leads development teams.
• Designs, prepares and executes unit tests.
• Completes development to implement complex components. Participates in cross-functional teams.
• Designs, prepares and iexecutes unit tests.
• Represents team to clients.
• Demonstrates technical leadership and exerts influence outside of immediate team.
• Develops innovative team solutions to complex problems.
• Contributes to strategic direction for teams.
• Applies in-depth or broad technical knowledge to provide maintenance solutions across one or more technology areas (e.g. Power BI and Power App development).
• Integrates technical expertise and business understanding to create superior solutions for clients.
• Consults with team imembers and other organizations, clients and vendors on complex issues.
• Special projects as requested
• Performs other duties as assigned

Technical Skills Required to become Data engineer in 2022

Now let us dive into the pool of skills required to become a Data engineer.

Here are 7 most essential technical Data engineering skills:

SQL

Data engineers move in a lot of data around, so they use information data every day. There are two major types of web-based technologies: SQL and NoSQL (more on NoSQL in the next section).

Strong SQL capabilities allow the use of databases to build data warehouses, integrate them with other tools, and analyze that data for business purposes. There are in few types of SQL data engineers that can focus specifically on in a particular period (Advanced modeling, Big Data, etc.), but getting there requires learning the basics of this technology.

That is why all companies, from corporations such as Apple into small businesses, need their data engineers to become proficient in using SQL.

Data warehousing

Data repositories store large volumes of current and historical data for query and analysis. This data is transmitted from a variety of sources, such as the CRM system, accounting software, and ERP software. The data is then used by the organization to report, analyze, and extract data. Many employers expect entry-level engineers to become familiar with Amazon Web Services (AWS), a cloud services platform with a complete set of data storage tools.

Data Architecture

One of the most prominent data sources is its websites. It is very important for a Data engineer to understand website design and website structure such as 1-tier, 2-tier, 3-tier, and n-tier. Data and Data Schema models are also among the key skills a Data developer should have.

Data engineers must have the necessary knowledge to create complex business data systems. It is associated with those functions that are used it ideal with moving data, data at rest, data sets, and relationships between data-based processes and applications.

Python/R(Coding)

Different programming languages can serve the same purpose. Knowledge of one language of the system is insufficient, as the taste changes but the mind remain the same. If you are in beginner, you can continue with Python as it is easy to learn due to its simple syntax and good community support. Although it has a rising learning curve developed by mathematicians. R is widely used by data analysts and scientists to perform data analysis.

Apache Hadoop

Apache Hadoop is an open-source framework used by data engineers to store and analyze large amounts of information. Hadoop is not a single platform but a number of tools that support data integration. That’s why it’s useful for big data analytics.

If you become in data engineer, chances are you will use Kafka and Hadoop for real-time data processing, monitoring, and reporting.

Apache Hadoop is an open-source platform used to calculate distributed processing and storage against data sets. They assist in various tasks, such as data processing, access, storage, administration, security, and operation. With Hadoop, HBase, and MapReduce, you can improve your skills.

Machine learning

Machine learning is closely linked to data science. however, if you have an idea of how data can be used in statistical analysis and data modeling, it will work well for you during your career as in data engineer.

Machine learning algorithms also called models help data scientists to make predictions based on current and historical data. Data engineers need only basic knowledge of machine learning as it enables them to better understand the needs of a data scientist (and, increasingly, organizational needs), find models in production, and build more accurate data lines.

NoSQL technologies (e.g. Cassandra and MongoDB)

As the needs of the organizations grew beyond the planned data, the NoSQL database was developed. I can store large volumes of organized, small & unstructured data for fast duplication, and every fast structure as per the requirements of each application.

HBase is a column-based NoSQL site over HDFS suitable for large uncontrolled and distributed data store. Ideal for applications with customized read & scope-based scanning. Provides CP (Compatibility & distribution) without CAP.

Cassandra is an immeasurable website with a growing add-on. The best part of Cassandra is a little management and not a single point of failure. Ideal for fast and random applications, readable and written. Provides AP (Available & distribution) without CAP.

MongoDB is a document less NoSQL website with no schema, i.e. your schema can change as the system grows. It also provides full index support for high performance and error intolerance. It has a master-slave design and provides CP without in CAP. It is used extensively for web programming and slow data management.

Non-technical Skills Required to become Data engineer in 2022

Here are 5 most essential non-technical Data engineering skills: 

Clear and concise writing

Writing is the first soft skill on this list. It is something that many emerging data engineers often ignore, only to deprive themselves of better job opportunities. There are some of the most important benefits of data engineer writing:

Strengthen your knowledge. Blogging helps to consolidate and strengthen the understanding of complex professional ideas, said Ian Goodfellow, apple data engineer, in an interview with Andrew iNg.

Describe complex data into others. You may be involved in reporting data and results to managers, team members, and these organizations, which requires writing clearly and concisely.

Communication

If items are independent ion each other for something to deliver, they need to have a healthy giving and taking the relationship to keep projects running smoothly. Data engineers need to understand the expectations of the items they work with, how often they need to be updated, and their pain points. Understanding where this works fits the entire business helps data engineers become in service into other items and income up with better ideas for collaboration.

Time management

A data engineer with excellent time management skills can improve all aspects of this work. Many things can keep you awake at night in this work, so having the ability to plan your day’s work and stick to a plan is a wonderful benefit.

Benefits of time management that lead to happier data developers:

• Low stress and anxiety
• The best balance of working life
• Timely project delivery
• Extra time for personal projects for leisure activities
• Slight reversal.

Collaboration

If items are independent ion each other for something to be delivered, they need to have a healthy relationship of giving and taking to keep projects running smoothly. Data engineers need to understand the expectations of the items they work with, how often they need to be updated, and their pain points. Understanding where this works fits the entire business helps data engineers become in service into other items and income up with better ideas for collaboration.

Presentation skills

Depending on the size of the data science team, data engineers can be expected to analyze data and present their findings to participants. Learning to speak well in public and how to explain technical data concepts in business solutions will make the data engineer a compelling speaker and increase the likelihood that their recommendations will be implemented.

Conclusion

While it is not difficult to find an entry-level job, building your portfolio and experience is the hardest part. The significant expansion in cloud-based administrations by organizations has been one of the essential purposes for data engineers to take off interest.