The world has seen an emergence in the amount of data available today, with every big company clocking millions of customer transactions. This ‘Big Data’ generated has revolutionized the way businesses run. Data is everywhere around us, which has led to a demand for professionals who can analyze and provide valuable insights to organizations.
Even though Data science is a buzzword in the industry, and the job of a data scientist is known as the sexiest job of the 21st century, The demand for data engineers has been rising at an alarming pace. Google has noted that the machine learning and Artificial Intelligence market will be around $1.2 Billion by 2023; the need for skilled data engineers will be eminent. Let us look at what the coming field is all about and why you may want to pursue it.
What is Data Engineering?
In the last decade, the world as we know it has undergone a complete digital transformation. Organizations produce massive volumes of data, and the data collected must be stored and used under compliance rules. Security and privacy of data is also major challenge. While data scientists analyze and generate results from the data, there is a need to organize and ensure the quality, security, accuracy, and consistency of the data in an organization.
This is where data engineering comes into the picture.
Why are companies hiring data engineers?
Even though the job of a Data scientist is known as the sexiest job in the 21st century, It is not the only data science-related job out there in the market. According to the data science interview survey by interview query, data science interviews grew by 10%, indicating a steep decline after increasing by 80% year over year. In comparison, Data engineering-specific interviews increased by 40%.
Let’s take a look at why there is an increase in demand for data engineers in the industry.
Over the last decade, organizations, irrespective of their domain, have undergone a digital transformation, trying to leverage the trending technologies to optimize their solutions and offerings to the current market. While in the earlier days, data scientists were expected to build data pipelines. And their tasks of analysis and processing of data. This resulted in redundant and inconsistent work among data scientists, and these issues led to companies being unable to extract optimal business insights. With the introduction of Artificial intelligence, the Internet of things, and cloud technologies, the amount of data generated in structured, semi-structured, and unstructured formats is massive. Organizations have been allocating funds to build adequate infrastructure and data architectures to utilize the large volumes of data at their disposal.
According to the IDG cloud computing survey 2020, 81% of companies use cloud infrastructure and house cloud applications, compared to 73% in 2018. This figure is expected to increase by 6% in 1 to 3 years. According to the survey on cloud computing,
Organizations, on average, will spend $78 million on cloud computing, compared to $ 73 million in 2020. The large influx of investments has led to the need for organizations to have proper infrastructure and skilled data scientists who can organize and maintain this immense volume of data. This demand is expected to be on the rise soon.
Increased Pay and Job Growth
According to a salary study conducted in 2021, Data engineers and Big data specialists have a median salary higher than AI and Data science specialists. The median wage for Data engineers in 2021 is 14.9 Lakhs per year, an increase of 2.3% from last year. Big data specialists draw a salary of 14.8 lakhs per annum, which shows a rise of 3.0% from the previous year.
|*Number of Job postings||*Number of candidates||Number of candidates/ number of job postings|
* LinkedIn job search results for the following keywords in the exact match: “data engineer,” “data scientist” in Geography: India. All searches were made on 26/04/2020.
LinkedIn search for ‘data engineer’ and ‘data scientist’ shows that there are 5.79 suitable candidates for every open data engineer job. Compared to 2416.6 candidates for every data scientist job opening. The contrast is dramatic, showing the scarcity of skilled data engineers. The number of jobs in India for data engineers is also higher than that of data scientists. These factors can help data engineers negotiate higher salaries during the recruitment process. This imbalance in the demand and supply has led to a steady increase in the industry’s demand for skilled data engineers.
What skills do you need to become a successful data engineer?
Data engineers require to be skilled in multiple areas. Here are the four skills necessary to be a successful data engineer:
- Programming: Data engineering requires proficient coding skills in programming languages like Python, Java, Scala, and R. Since data engineers are needed to handle databases and data warehouses, they must know Querying languages like SQL and NoSQL.
- Databases: Data engineers use databases for storage; hence they are expected to know the functioning of relational and non-relational databases.
- Extract, Transform and Load (ETL): ETL is the process used to move data from databases to data warehouses. Data engineers work with ETL Tools like Apache Airflow, Hadoop, Stitch, Talend, etc.
- Automation: Automation plays a vital role in large organizations with big data due to the sheer volume. Data engineers need to write scripts to automate repetitive tasks while handling large volumes of data.
Other skills like working knowledge of Machine learning and cloud computing can help data engineers perform their duties easily and better understand the organization’s requirements. A crash course in technologies like AWS and Google Cloud can benefit data engineers.
Career Path of a data engineer
The Dice Tech Job Report in 2020 lists data engineering as the fastest-growing job in technology, with a predicted 50%growth in the number of open positions. With the increase in the demand for skilled data engineers. You can start your data engineer career path, starting at entry-level positions like BI analysts or software engineers. With enough experience, one can progress quickly to become a skilled data engineer, data architect, or machine learning engineer. Here is a look at the salary trajectory for a data engineer, as per Indeed:
Salary of a Data Engineer.
According to Glassdoor, a data engineer can earn 4 Lakhs to 19 Lakhs per year. The average salary for an experienced Data engineer is ₹8,00,000 in India. Payscale quotes The average salary for a Data Engineer in India as ₹865193 per annum. According to Indeed, the average salary for a data engineer is ₹10,35,826 per year in India. According to Indeed, Cities like Delhi and Bangaluru record salaries as high as 12 Lakhs and 11 lakhs respectively for senior data engineers, while Chennai, Hyderabad, and Mumbai show a pay range of 10 Lakhs. Noida, Pune, and Kochi average salaries of 9 lakhs for skilled data engineers.
The average Data Engineering salary in the United States is $1,12,493 per year (Glassdoor), and the current range band for the US is between $76000 and $200,000. The average Data Australia & Singapore rank the highest with $1,09,500 per year SGD 5,763 per month, while India and China are the lowest, with ₹8,00,000 per year and CN¥20,020 /mo. Germany’s average Data Engineering salary is €62,637 per year, and the current range in Germany spans between €49000 and €77,000. The national average salary for a Data Engineer is $87,533 per year in Canada, whereas a Lead data engineer can earn up to CA$1,16,460 /year. The national average salary for a Data Engineer is £48,481 per year in United Kingdom.
Data engineering is a specialized field, and with the increase in popularity of artificial intelligence and data science. The need for skilled data engineers is increasing every year. According to the LinkedIn 2021 US Jobs on the Rise report, data engineering roles have shown a 35% increase in demand. With the right skill set, you can work in data science while playing a pivotal role in providing. Organizing data for organizational needs. With proficient skills in coding and databases. You can start building a portfolio by working on data engineering projects. Set yourself on the path to becoming a successful data engineer.
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