At present, organizations are hunting for data as well as data experts to analyze the market requirements, explore the meaningful insights, predict the behavior and making careful decisions from data. Data Engineers and Data Scientists work as a team, who transform the raw data in ways that provides their organization with a competitive edge. But the data engineers and scientists have different roles and responsibilities, academic background, key skills and pay scales.
Now, if we compare Data Engineer vs. Data Scientist, we need to consider the following points:
Roles and Responsibilities Academic Background Technical Skills Jobs Prospects and Salary Trends Top Recruiters
Roles and Responsibilities A Data Engineer is involved in different tasks such as data preparing and processing, model development; generate tests to validate & maintaining the complete architecture. They focus on creating a full-stack for data collection, storage, processing in real-time or in batches. They usually employ software tools and programming languages to build API’s for processing of data from real-time and large databases.
A Data Scientist is involved in data pre-processing, hidden pattern finding, organizing the large datasets to answer the business queries, analyzes and interpret complex data. Besides this, Data Scientists perform descriptive statistics and automate the model accordingly to fulfill the organizational and customer need. They also utilize artificial intelligence and machine learning models for better prediction, classification, and clustering.
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Data Engineer
Data Scientist
To explore data acquisition opportunities.
To develop reliable operational models.
To develop, deploy, test & maintain data architectures for real and large systems.
Conduct research to answer the organization and business queries.
Use different Software tools and scripting languages to combine systems together.
To find out the hidden pattern from huge data.
Explore new approaches for data acquisition, and build new applications for existing data.
Carry out data analytics and optimization using Artificial intelligence & machine learning.
To build pipelines for different ETL operations.
Deployment of more sophisticated analytics programs, and executing machine learning, and statistical methods.
Monitoring data collection, storage, as well as data retrieval process.
Performs descriptive statistics and automate the model accordingly.
Ensures data accuracy and flexibility.
To make a bridge between the stakeholders and customer.
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Academic Background Data Engineers are required to be a graduate in Computer Science concepts with a strong academic and analytical background. Data Scientists are supposed to be a graduate in engineering sciences viz. computer, social, and physical along with good control over statistics. However the people with higher educational background like M.Tech or Ph.D are preferred at higher levels in industry. Apart from these basic academic qualifications, certifications in the area of Data Science give strength to the candidate profile.
Technical Skills Data Engineers should have sound knowledge of programming language: Java, Python, Scala, JavaScript and so on. Apart from the programming language, they should have a sound hand on databases: Oracle, MongoDB, Hadoop, firebase, Hive, DashDB, Cassandra, MySQL and so on. The Data Engineer should also have in-depth knowledge of Data Warehousing, Data architecture, ETL and pipelining processes.
A Data Scientist’s skills are more analytics oriented in comparison to Data Engineer. Data Scientist should have a strong Statistics and Mathematics background. The desired programming languages are R Programming, MATLAB, SAS, Python programming, Julia, SCALA and so on. They should have strong hands-on Hadoop based analytics, Statistical analysis, data mining and Machine Learning approaches.
Job prospects and Salary Trends Data Engineers and Data Scientists both are having enormous job prospects on handsome salary packages. The various study and surveys have been conducted to explore the demand for a Data science professional’s such as:
According to PWC, there is a demand of around 2.7 million data science professionals by 2020.
According to the U.S. Bureau of Labor Statistics, 11.5 million new job offerings by the year 2026.
Source: Analytics India Magazine [2]
In 2019, 97,000 jobs are vacant due to a shortage of skilled data science professionals.
Source: Analytics India Magazine [2]
According to IBM, the demand for professionals will be increased by 364,000 to 2,720,000 in 2020.
According to Linkedin’s top 15 emerging jobs list in the USA, Data Scientist has 37% and Data Engineer has 33% annual growth.[5]
The surveys and studies clearly show that both Data Engineer, as well as Data Scientist, is in huge demand and it will remain to continue in the coming years.Now, let’s see the salary trends for these jobs.
According to the Dice Salary Report, the average salary of a Data Engineer is $113,249 and Data Scientist is $106,298
The average salary of a Data Engineer as reported by Glassdoor is INR 8,59,773 and that of a Data Scientist is INR 10,27,806.
The average salary of a Data Engineer as reported by PayScale is INR 8,54,650 and of a Data Scientist is INR 8,15,427.
Top Recruiters There are a few leading industries, which are posting the highest number of job openings for a data science professional in the present and past years.
Accenture
Amazon
KPMG
Honeywell
Wells Fargo
E&Y
Dell International
eClerx Services
Deloitte
Capgemini
Reference :-
www.datacamp.com
www.analyticsindiamag.com
www.techhub.dice.com
www.data-flair.training
www.linkedin.com
www.insights.dice.com
www.glassdoor.co.in
www.payscale.com
