Big Data Engineer Salary – Career Path

Last updated on 16th Jul 2020, Blog, General

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What is a Data Engineer?

• Data engineers build and maintain data pipelines, warehousing big data in such a way that makes it accessible later on. This infrastructure is necessary for every other aspect of data science.
• The data engineer develops, constructs, maintains, and tests architecture, including databases and large-scale processing systems.
• The data set processes that data engineers build are then used in modeling, mining, acquisition, and verification.
• The data engineer works in tandem with data architects, data analysts, and data scientists.
• Data architects are in charge of data management systems, and understand a company’s data use, while data analysts interpret data to develop actionable insights.
• Finally, data scientists focus on machine learning and advanced statistical modeling. They must share these insights to other stakeholders in the company through data visualization and storytelling.

What does it take to be a Data Engineer?

• The primary job of a Data Engineer is to design and engineer a reliable infrastructure for transforming data into such formats as can be used by Data Scientists.
• Apart from building scalable pipelines to covert semi-structured and unstructured data into usable formats, Data Engineers must also identify meaningful trends in large datasets. Essentially, Data Engineers work to prepare and make raw data more useful for analytical or operational uses. There are many myths about data engineers and most of them are far from reality.
• Read more about the myths and reality of data engineers.
• In an organization, the position of a Data Engineer is as vital as that of a Data Scientist. The only reason why Data
• Engineers remain away from the limelight is that they have no direct link to the end product of the analysis.
• While the specific tasks of a Data Engineer can vary from one company to the other, they share some common responsibilities, including: 
• Integrate, consolidate, and cleanse data collected from multiple sources.
• Prepare raw data for manipulation and predictive/prescriptive modeling by Data Scientists.
• Develop the necessary infrastructure for optimal extraction, transformation, and loading of data from disparate sources using SQL, AWS, and other Big Data technologies.
• Deploy sophisticated analytics programs, machine learning algorithms, and statistical techniques to build data pipelines.
• Assemble vast and complex data sets to cater to the functional and non-functional business requirements.
• Identify and develop innovative ways to improve data reliability, efficiency, and quality.
• Develop, construct, test, and maintain data architectures.
• Rethink and redesign existing frameworks to optimize their functioning.
• Align data architecture to fit perfectly with business requirements.
• Conduct industry research to stay updated with the latest market trends.
• Collaborate with co-workers and clients to determine the requirements of projects.

Skills needed to become a Data Engineer

• Building and designing large-scale applications
• Database architecture and data warehousing
• Data modeling and mining
• Statistical modeling and regression analysis
• Distributed computing and splitting algorithms to yield predictive accuracy
• Proficiency in languages, especially R, SAS, Python, C/C++, Ruby Perl, Java, and MatLab
• Database solution languages, especially SQL, as well as Cassandra, and Bigtable
• Hadoop-based analytics, such as HBase, Hive, Pig, and MapReduce
• Operating systems, especially UNIX, Linux, and Solaris
• Machine learning, including AForge.NET and Scikit-learn

Clearly, data engineers are expected to have a wide array of technical expertise. Much of the job, though, requires critical thinking and the ability to solve problems creatively so that the right approach is used in the right situation. This might include creating solutions that don’t yet exist.

• According to Glassdoor, the average Data Engineer salary in India is Rs.8,56,643 LPA. But of course, the Data Engineer salary depends on several factors, including company size and reputation, geographical location, education qualifications, job position, and work experience.
• Reputed companies and big players in the Big Data industry like Amazon, Airbnb, Spotify, Netflix, IBM, Accenture, Deloitte, and Capgemini, to name a few, usually pay high compensation to Data Engineers. Also, the more your past work experience in Big Data, the higher will be your market value.
• Despite the global demand-supply paradox (the demand for Data Engineers far exceeds their supply), the career prospect of Data Engineers looks promising in India. According to Analytics India Magazine report,

 “While IT firms have shown a negative trend, the demand for data engineering professionals has increased across the companies, resulting in a significant jump in their salary structure. Whereas for salaries across analytics skills, advanced analytics roles and predictive modeling professionals grabbed the limelight compared to other roles.”

•  Due to the shortage of adequate talent in the field, companies are ready to pay huge remuneration to freshers and mid-level Data Engineers as well.
• Going by PayScale stats, an entry-level Data Engineer with less than 1-year experience can earn an average annual salary of Rs.4,00,676 LPA.

• As for Data Engineers in their early career (1-4 years of experience), they make anywhere around Rs.7,37,257 LPA. As they proceed to mid-level (with 5-9 years of experience), the salary of a Data Engineer becomes Rs.1,218,983 LPA. Data Engineers having over 15 years of work experience can make more than Rs.1,579,282 LPA.

Source 

•  Another substantial progress on the ground of compensation is that the percentage of analytics professionals with a salary package of less than Rs. 6 LPA has reduced significantly.
• As of now, 37.6% of analytics professionals in India make less than Rs.6 LPA, which is lower than what it was in 2017 (39%) and 2016 (42%).