How Data Engineering Is Different From Data Science?

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Data Science Roles in a Corporate: It is too confusing!!!

I started off my corporate journey with a title called Associate Consultant and I specialized in Supply Chain management; to be very specific into what industry call indirect procurement. Two years back I decided to change my track because I did not find that thrill that an engineer is expects to have; being a consultant giving consulting services to companies and organization.

I decided to get into the world of data specially into data science because that was exactly what I knew about data science jobs in the industry. Reality struck hard when I started researching on the various roles associated with Data science Course in the IT industry.

I was thrilled to learn that data science is the general term which encapsulates many other titles such as Data Engineers, Data Analysts, Machine Learning Engineers, etc. What I thought of as one role in the industry was actually a role that needed expertise in many fields, being a data scientist.

I see a similar condition for many young graduates who complete their degree and venture out in search of jobs in the leading corporates. A similar thing happens for young professionals who just started their careers and perhaps wanted to switch the same to data science, however, they are in a better position because they are already in the industry and getting an access to industry professionals becomes easy and they are more readily available.

Adding on, many corporates believe in the concept of reskilling and cross skilling which ensures employees getting trained on recent technologies. Nevertheless, students and young graduates have no such privileges and hence they often end up being confused and left in the dark as so what various roles have to offer them in terms of challenges and career growth.

It also creates confusions as to how one role is different from the others such as how machine learning engineers are different than the data engineers or how data scientists are different from data engineers?

The main reason behind so many distinct roles being currently created in any organization which deals in data science is perhaps related to the fact that industries want to acquire as many talents as possible to make their data science services as robust as possible.

The only way that this can be done is to segregate different aspects of a data science projects into different departments and let one owner execute that aspect of the project instead of a limited number of individual driving a project under a single banner called the data scientists or the data analysts.

Another reason that has given rise to so many roles in the data industry is the ever changing perspective and the way organizations want to use the captured data. Some years back the focus was to primarily figure out trends so that organizations are better prepared to handle them whereas, in the current scenario the focus has changed to not only get insights but to build models that can predict the future to making intelligent applications to enabling the applications to adapt to the ever changing demographics of the data and the sources from which they originate.

All of these technological advancements that was once thought to be a science fiction has now turned into a reality and in order to make this into a reality, organizations had to introduce a variety of roles to incorporate all the complexities.

In this article, I will try to get into as much detail as I can with the two roles that sound similar but actually are not. Data Engineering and Data Science can be considered interdependent on each other to some aspect but on a broader perspective they are not and we will see how.

I will talk about how Data Engineering is different from Data science. I will cover this with the help of three factors which sets both Data Engineering and Data Science apart,

• The job profiles for Data Engineering and Data Science.
• The average pay scale for Careers in Data Engineering and Data Science
• The pathway to Data Engineering and Data Science

Hopefully, this will give you a clear picture of what Data Engineering and Data Science roles deal in and what are the skillsets needed to get into these roles. But before getting started with the differences between the two roles in the data industry, let us first have an overview of what each roles deals in and how each of these roles contributes to the overall picture.

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Data Engineering: A Bird’s eye view

 

Data Engineering is the aspect of data science that focuses on mechanisms that captures data, validating data and also making sure the data is not erroneous. As is the case with most of the other roles of data science, data engineering is more of a practical aspect of data science which does not involve data analysis, even if it does in some cases it is limited to very menial data analysis which comes handy when data scientists or data analysts start analyzing the data.

Data Engineering is more focused on building data sets which later results into a comprehensive understanding of the data. They are usually experts in the one or more of the following fields,

• Computer and system architecture
• Database administrators
• Database designers
• Experts in integrating one application with others so that data flows seamlessly

• Data modelling
• Database theory and administration
• Database reporting
• ETL practice in real time data
• Architectures: Computers, systems and integrated environments

Data Scientists: A Bird’s eye View

Data Scientists are the story tellers of the corporate world who are experts in creating compelling visualizations and project such intense convictions while telling the story from the data that even big shots in any organization nod their head in tandem with what is being said and delivered by the story teller. Data Scientists are considered to be the only species in the data industry who are paid because they are a jack of all trades and master of none.

One can consider data scientists as a part time mathematician, part time statistician, part time programmer, part time computer engineer, part time business analysts and part time thinkers. They act as bridges between the data analysts and the business and can handle both aspect of the same coin. They are highly motivated professionals with a knack of finding out as many trends as possible from a given chunk of data.

• Programming
• Data Analytics
• Machine Learning
• Data Visualization and reporting
• Statistics and Mathematics
• Algorithms
• Data Cleaning and data munching
• BigData: Tools, platforms and concepts
• Cloud computing
• Data Warehousing
• Effective communication skills

In essence, a data scientist is someone who not only analyses the data and get insights from it but also are effective communicators who communicate their findings as fluently as possible.

Now that we have seen an overall picture of both Data Engineering and Data Science and their key roles and responsibilities in the data world, let us now get into the details of the three factors that sets the fields different from each other.

The job profiles for Data Engineering and Data Science

One of the most fruitful way to understand how Data Engineering is different from Data Science is to understand the job profiles of both data engineering and data science.

Job Profile in Data Engineering

The usual job description of a data engineer in any corporate mainly focuses in building a robust infrastructure and pipeline of data which enables the data scientists to do their jobs. The data engineers are the ones that set up data flows and data pipelines so that the data gets captured and stored which is later retrieved by the data scientists for further insights and investigation.

Data Engineering not only deals in building a solid data infrastructure but also plays a key role in optimizing the same in the corporate set up. The data engineer often engages with different portfolios and experts in the IT landscape which often include software developers, database admins, data analysts and data scientists and ensures all the data related architecture in any of the fields is optimized. In some extreme cases, data engineers are often engaged in a complete redesign of the entire data infrastructure of the organization.

The key responsibilities that are generally seen in the job description of a data engineer are as follows,

• Building and maintaining a robust data pipeline and a solid data architecture.
• Collection and wrangling of data to meet business and technical requirements of the underlying processes and business tools.
• Identify areas of improvements of the data infrastructure; automate manual data related jobs; make the data infrastructure scalable and efficient.
• Make the data infrastructure optimal for Extraction, Transformation and Loading of the data from a wide array of data collecting sources.
• Work with business stakeholders including management, product design teams, software development teams and data analytics teams and help them out with their data related needs.
• Working with the security teams to ensure the data is secured across nationalities and data centers.

The key working experience that is usually associated with the job description is as follows. Please note that the qualification requirements might vary depending on the job level, years of experience and the position for which the hiring might take place.

It also depends on the organization, the number of employees, the clients and the industry in which it operates. These may change from time to time and depending on the requirements.

• Knowledge in advanced SQL and an experience in working across different relational and non-relational databases including structured and unstructured databases.
• Experience in Big Data technologies including big data pipelines and data sets.
• Ability to dive deep into the internal and external processes of the organization and generate reports to answer specific questions and also find out areas of improvements.
• Working experience with unstructured data.
• Build processes which complement the Extraction, Transformation and Load of the data.
• Ability to integrate large and complex data sets from a wide variety of related and non-related data sources.
• Experience in working across cross functional teams and the ability to work under pressure.
• Experience with Big Data tools such as Hadoop.
• Experience with unstructured databases such as PostgreSQL and Cassandra.
• Experience with tools like Azkaban, Luigi, Airflow, etc. and related workforce management tools.
• Experience with AWS.
• Experience with Streaming technologies and tools.
• Experience with programming languages such as Python, R, Scala, etc.

Job Profile for a Data Scientist

The job description of a data scientist particularly focuses on analytical, statistical and programming skills because at the end of the day it is the data scientist who brings out the trends and visualizations from the data so that a well-crafted story is communicated to the business stakeholders for decision making.

Data Scientists are known for their love towards their data and they analyze the data to provide insights and develop data driven solutions to the problems at hand. Data Scientists are expected to have knowledge on a wide spectrum of concepts and technologies including machine learning, programming languages, databases, statistics and reporting.

Usually, a data scientist is expected to work with cross-functional teams including sales, marketing, product and executive leadership of an organization to answer specific questions to gain business insights. The data scientist uses data models to develop optimization opportunities on a product and process level. They can execute simulations on large and complex data sets to develop predictive models and generate insights to help answer questions and optimize business outcomes.

The key responsibilities of a data scientist are the following,

The key working experience and qualifications of a data scientists are as follows,

Please note however that the above mentioned experience is very generic in nature which can be found in several job descriptions from a variety of organizations. Please also note that there might be categories that might change depending on the business logic and requirements.

The average pay scale for Careers in Data Engineering and Data Science

A Career in Data Engineering

As per recent statistics from glassdor, the average salary of a data engineering professional results to be around $137.776 per year with a range between $110,000 per year to $155,000 depending on the seniority, skills, hierarchy and type of work the professionals are expected to perform on a daily basis.

Keeping in mind the same statistics, the average salary of a senior data engineering professional happens to be around $172,603 per year with a range between $152,000 per year to $194,000 per year. If we drill down this generalization to leading MNCs across the globe, we get the below results,

Sl. No:	Organization	Salary Range	Average Salary
1	Amazon	$78,000 to $133,000	$103,849
2	HP	$64,000 to $105,00	$86,164
3	Facebook	$93,000 to $171,000	$122,695
4	IBM	$90,000 to $116,000	$99,351

A Career as a Data Scientist

According to Datajobs.com the average salary of a Data Scientist is about $127,500 per year with a range of $85,000 to $170,000 depending on skills, seniority, the organization and the sector. As per the same website the highest paying sectors for a data scientist are as follows,

• Cloud Services
• Social Networking
• Banking and Finance

Depending on the skillsets, a data scientist working in these domains is expected to earn more than the average salary in some cases. Some of the highest paying tech organizations are as follows,

• Google: $152,856
• Apple: $145,974
• Twitter: $135,360
• Facebook: $134,715
• PayPal: $132,909
• Airbnb: $127,852
• Microsoft: $123, 328

Having talked about the average salaries of professionals working in both the domains namely Data Engineering and Data Science, we should also note that the average annual pay scale of a professional is widely dependent on the type of organization, the size of organization, the type of data which is in reference and the type of insights one is expected to dig out of the data.

The pathway to Data Engineering and Data Science

Now that we had an insight into Data Engineering and Data Science in terms of the roles and responsibilities and the average pay scale, let us know have a peek into what it takes for someone to get into fields like Data Engineering and Data Science.

The Pathway to Career in Data Engineering

According to David Bianco, a data engineer veteran,

“Languages come and go, so it is better to gain a full understanding of the concepts behind building a robust pipeline”

And if one is to follow this advice, one has to come out of the myth that data engineering is all about learning to code and learning the technical know-how behind the data pipeline. Below are the steps that one can follow towards becoming a data engineer,

The Pathway to Career as a Data Scientist

A Data Scientists as experts like to call are jack of all trades and may be master of none. A Data Scientist is obsessed with data and obsessed with finding more and more insights by delving into the data.

Since the role of a Data Scientist is not only limited to the technical abilities of getting insights from the data, there are a lot more competencies that a data scientist should possess such as leadership skills, project management, analytical storytelling, strong communication skills to communicate the results and persuasion skills to persuade the audience to adapt to their findings. The pathway to being a data scientist can be broadly divided into six steps which are as follows,

Data Engineering and Data Science: A Conclusion

Now that we have looked into the problem “How Data Engineering us different from Data Science?” I hope it is pretty clear as to how Data Engineering and Data Science fields are different from each other in terms of the three parameters we looked into in detail. We looked in the below parameters to understand both the fields are different from each other,

In conclusion, data engineering and data science are very different from each other. On one hand where data engineering focus mainly on the data infrastructure, data flows and data pipeline, data science is more of a science that engulfs the entire data analytics process right from visualizing data to getting insights to communicating the same to the stakeholders so that everyone is on the same page.

Data Engineers can be seen more of data enablers which help data scientists get relevant data in proper format and shape so that insights can be extracted from the data.

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