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Skills Needed to Become a Data Engineer

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These days, many companies, which are looking to maximize their business potential, are focusing more and more on data. And, data science is the new kid on the block that has become a firm favorite for companies across the globe. 

Notably, data science is important for companies to handle huge amounts of information, including customer data or financial data. Not just this, it involves using various data tools to help businesses understand the information and make important decisions.

Both small and large corporations are making use of data science to improve their businesses. So, the need for data scientists is huge, which makes it one of the most in-demand career paths in the Information Technology sector at present.

But first, let us look at why companies focus on data so much.

 

Why companies rely on data-driven decisions?

To withstand the never-ending race to win customer’s hearts and a stronghold in the current market, companies are relying on data. According to an analysis conducted by the McKinsey Global Institute, the organizations that are powered by data-driven decisions are 26 times better in acquiring customers than their peers. Their customer retention is six times more than others, and as a result, they will be 19 times more profitable than the rest.

In recent years, many organizations have understood that data is their most prized asset. Thus, now, data scientists and analysts track all the minute details of their business. The data points are analyzed to identify patterns and characteristics. Also, the details help companies to understand their business performance and identify the areas where they lag.

The benefits of data-driven decisions are:

Better decision-making: 

After collecting data from surveys, user testing, and product launches, important decisions about the product are taken. With data science, all the associated data is accumulated, cleaned, and analyzed. Key areas, such as customer services, operations, and finance, are identified from where data is to be collected. Then, the data is further analyzed using methods like predictive analysis to obtain future insights. 

So, every decision is backed by data to reduce the scope for intuition or gut instincts. As a result, more accurate decisions are taken. 

For example, Google created a department called People Analytics Department. The aim was to make better human resource decisions. It used data from employee surveys and performance reviews to select managers. It used this to hire better managers whom employees loved and liked working with.

Managing costs: 

Data helps companies to track expenses like sales, marketing, promotion, logistics, human resource management, and R&D. Based on data patterns, a company takes important decisions like staffing and the best time for product launches. For example, it might be observed that the sales activities have been higher in certain months than others. 

The company might look to hire more executives during these months to enhance sales and marketing. Additionally, it might look to launch products during these months when the demand is the highest. It helps to increase revenue and also identify areas where the company might be spending more.

Reaching out to more customers: 

Companies are able to connect more with their customers and cater to their needs through data. For example, global coffee giant Starbucks uses social media platforms like Facebook to understand customers. It offers free music downloads and app downloads to their most loyal followers on social media. The company takes feedback from the reviews and comments the customers post about it on Instagram and Twitter.

Let us take a closer look at a crucial job role in the world of data science – Data Engineer.

What does a Data Engineer do?

Data engineers are responsible for maintaining, creating, and testing architectures, such as databases. From small relational databases to large petabyte sized data lakes, they handle it all. They manage and organize raw data. A data engineer might come across inconsistencies like coding errors and may need to come up with ways to solve these problems.

The job role requires a solid command of programming, databases, computer science, and mathematics. The common day-to-day responsibilities of a data engineer include:

  • Aligning the data architectures and systems with business goals
  • Identifying more opportunities for data acquisition
  • Creating data set processes for data mining and modeling
  • Ensuring that the ETL – Extraction, Transaction, and Load processes are properly executed  
  • Figuring out ways to enhance data quality, readability and efficiency
  • Working on large datasets to tackle business issues
  • Identifying hidden data patterns and discovering more opportunities for automation
  • Executing high-end analytics, statistical and machine learning algorithms
  • Preparing datasets for conducting predictive modeling
  • Communicating with data scientists, managers, and other stakeholders to ensure all business objectives are met

Skills needed to become a data engineer

To become a data engineer, you need to have a solid background in applied mathematics, engineering, computer science, or related IT fields. Here are the crucial areas every data engineer needs to be skilled in:

Database architectures: 

A strong foundation in architectural database concepts, such as 1-tier, 2-tier, and 3-tier, is required. In-depth knowledge of SQL and database schema is also important. You need to know PL/ SQL and NoSQL technologies, such as HBase, Cassandra, and MongoDB.

Programming: 

Knowledge of programming languages, such as Python, Golang, Java, C/ C++, R, and Perl, is a plus point.

Data warehousing tools: 

You need to know about data warehousing to handle huge datasets that are coming from various sources. As a data engineer, you need to perform ETL operations on the data. Warehousing tools like Panoply, BiQuery, Redshift, and Looker, are popularly used. Similarly, you have to learn ETL tools, such as IBM InfoSphere DataStage, Microsoft SSIS, Stitch Data, Snap Logic, and Segment.

Big Data frameworks: 

Another crucial requirement for being a data engineer is knowing about Big Data frameworks. Some of the tools you must know about are:

  • Apache Hadoop
  • MapReduce
  • ApacheStorm
  • HIVE and PIG
  • Spark MLlib
  • Apache HBase
  • Oozie
  • Flume 
  • Sqoop
  • YARN

Operating systems: 

You are required to know various operating systems, such as UNIX, LINUX, Windows, and Solaris. This is because some data operations require root access to the operating system and hardware.

A data engineer is one of the highest-paid jobs in the IT sector in India. With an average salary of INR 7,21,023 per year, there are more than 14,000 jobs in India. Also, there are more than 120,000 jobs in the United States, where the salary ranges from $70,000 to $125,000.

Therefore, even if you have a degree in computer science or mathematics, obtaining certifications or courses for data engineering will be valuable. Here are some courses you can opt for:

  • Big Data Engineering Courses
  • IBM Certified Data Engineer
  • Cloudera Certified Professional (CCP)
  • Google Cloud Certified Professional Data Engineer
  • Certificate in Engineering Excellence Big Data Analytics Optimization

This may be the best time to start learning data engineering as the demand is very high in the global market. With the number of data jobs increasing day by day, enrolling in courses along with your degree course can set you up for a bright career.

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