People often get confused between data and Big Data. Many fail to realize how Big Data is becoming the next big thing in today’s world. Big Data refers to huge amounts of complex data that human and traditional methods fall short in compiling. Big Data refers to both structured and unstructured data. It could be a company’s entire infrastructure. While Big Data itself cannot be managed by an individual, someone who has pursued a data engineering course and learnt the right tools can quickly analyze, collect or monitor it.
Big Data can help to identify trends, predict future trends and compare current market standards. Doug Laney explained Big Data as being defined by the three Vs.
- Volume – Big Data contains data from multiple sources. This can include personal information, information related to transactions, purchases, videos, communications, etc.
- Velocity – Big data itself involves extremely fast-moving data. An example – during its Prime Day offers, Amazon receives an average of 6,000 orders per minute. This means every minute, 6,000 new user information and transactions are being added to their server.
- Variety – The data obtained can be anything from alphabetical, to numerical, to statistical.
Who is a Data Engineer?
Gartner reported that 2021 will be the Year of Hyper Automation, with AI taking the front seat. The projected growth rate for big data analytics is 12.3% between 2019 and 2027. But what is actually expected of someone with data engineer certification? Simply put, a data engineer is someone who structures the data of a company.
This is done via an ETL (Extract Transform Load). ETLs are like data warehouses and it is the job of the data engineer to create, manage and run them. Thus, a data engineer must have knowledge of Hadoop clusters, building stream-processing systems, existing Big Data tools, ETL techniques, and general computing principles.
So, what is this information used for? Let’s take the example of an eCommerce portal. A Data Engineer can structure the available information. By studying it, the data engineer or data scientist can now analyze user shopping patterns as opposed to their demographics.
They can now use this to offer personalized marketing assistance. And this in turn helps the company to increase their ROI while targeting a smaller and more specific customer segment.
Salary Prospects of a Data Engineer
Like most jobs, having more years of experience has an uplifting effect on the salary. Let’s take a look at the best countries for data engineers to thrive in based on their location.
The average pay scale for a Data Engineer in India is ₹835,889 per year. Those working at larger corporate companies can earn as much as ₹2,000,000 per year. To become a data engineer here, you will have to have completed data engineer training or have a Bachelor’s degree in IT or CS. Apart from this, you must be proficient in programming languages like Java, C++, and Python. An understanding of how data is managed in larger warehouses and ETLs is mandatory.
In the US, a data engineer can expect a salary of around $130,133 per year. However, the pay scale varies from company to company. Companies that work to provide customized content to users are generally the top-paying ones. In the US, Facebook offers the most lucrative and high-paying jobs with an average pay of $176,346 per year. This is followed by companies like LinkedIn and eBay. One must have at least a few years of experience in such a job before applying to larger companies.
Around the Asia-Pacific region, Australia offers the best opportunities with an average pay scale of $110K per year. In places like Sydney, it could be as high as $127,511 per year. Other cities that offer the best opportunities are Canberra ACT, St. Leonard’s and Mitchell ACT. Candidates are expected to have completed a bachelor’s in computer science along with data science specialization. Alternatively, they might need a few years of job experience, depending on the company.
Within the European Union, Germany offers the best pay scale for data engineers at an average of around €60,965 per year. For professionals, this number rises to €90,000 per year. However, Germany also has stricter hiring rules. Companies based in Munich generally look for candidates with up to 5 years of experience of full Hadoop understanding. People report high satisfaction rates with their state-of-the-art social security and support programs.
While Germany offers various university programs and opportunities for beginners, if you are a seasoned professional then Switzerland has the best opportunities in Europe. Reports until 2018 show that professionals in Switzerland were paid as much as €125,000 per year. One can apply for companies like Verizon, ACTS, and more. Typically, they look for mid-range professionals with a few years of experience.
Those looking for big data jobs within Asia can consider Singapore on their list. Singapore ranks second to Australia when it comes to big data salaries. The average pay scale is $80,000 per year. The top-paying companies include Facebook, the Standard Chartered Bank, CitiBank, and Lazada. You can get the highest monthly salary at Marina Bay, which comes to approximately $9,037 per month. This is followed by the cities of Changi and Raffles.
Overall, countries in Europe have a better pay rate than the US or the Asia-Pacific. You have to look at the salaries along with the cost of residing in these countries. In the US, one can also get cash bonuses during the year ranging to as high as $5,000 per year.
Germany offers the best job satisfaction. Within the Asia Pacific, New Zealand, and Australia perform the best. India on the other hand, has a lower pay scale but this is balanced out by the lower cost of living.
Specialization or completing a Master’s program in Big Data is essential in some companies. But for others, proving your experience is enough. With a proper data engineer training background, shifting fields is easier than ever before.