In today’s technology-driven world, data is everywhere, and its importance is only increasing. Whether you’re a startup or a large organization, data analytics are a vital tool for business success.
But who are Data Scientists and Data Engineers?
To answer these questions, we’ll discuss the different roles of a Data Scientist and a Data Engineer and explain when each is required and used.
We’ll also discuss how to determine whether a Data Scientist or Data Engineer is the right person for your organization and help you understand how to hire a Data Scientist or Data Engineer as part of your team.
Who Is a Data Scientist?
To understand more about the topic of “Data Scientist vs. Data Engineer,” you first need to know the basics of who they are.
A Data Scientist can be considered the most important professional in any company’s data analytics department. Data Scientists are primarily responsible for finding new insights from the data presented to help the organization make significant decisions.
Previously, Data Scientists gathered, processed, cleaned, and analyzed using structured and unstructured data sets. Nowadays, they focus on working on the data sent to them by the Data Engineers after cleaning. Therefore, you can see that their roles complement each other.
They look for patterns and trends in these data sets to gather insights that will help develop a business strategy or improve the quality of a product. Data Scientists have a firm grasp of mathematics, statistics, computer science, and programming to do this more efficiently.
They use machine learning techniques, programming languages like R and Python, SAS, and other data tools to interpret market trends, understand customer behavior, and improve business operations.
Roles and Responsibilities of a Data Scientist
Data Scientists work with data analysts, business intelligence professionals, engineers, and database administrators to maintain all the data generated by the organization. They handle all the data-related issues of the company.
Let’s look at their roles and responsibilities in detail.
Analytics
As a Data Scientist receives a cleaned and manipulated data set, they enter this data into Apache Hadoop or SAS tools. They perform prescriptive and predictive modeling using this data to uncover any hidden inside of a pattern with a set.
After analyzing the data, they also create statistical models to solve data issues. They perform data operations such as classification, clustering, sampling, projections, and pattern analysis. It is done to use the data to answer questions and explore other hidden patterns.
Communication
A Data Scientist doesn’t do everything alone; he needs to collaborate with other team members and communicate the results of the findings. So, after the data has been adequately analyzed, the results have to be sent over to the respective stakeholders, such as Data Engineers, software developers, or business strategists.
This might be done weekly, monthly, or daily. Data visualization tools such as Tableau or Microsoft Power BI can present the data insights and results simply.
These activities advance the business processes and improve the company’s decision-making. The stakeholders may suggest changes or ask for other insights from the Data Scientist during their interactions.
Strategizing and Ideation
Data Scientists play an essential role in helping other professionals develop business strategies. They try to understand a company’s target market, customer expectations, and market trends using data insights.
Then, they develop strategies to solve related business problems. Data Scientists also contribute to developing new ideas to improve the existing products or services. They might use the available data streams to help the ideation process.
Management
They play an essential role in managing all the data operations within the organization. Data Scientists might supervise the tasks performed by other data professionals, including engineers, data analysts, and database managers.
Data Scientists may also have to manage several data analytics projects, enhance their efficiency and determine flaws. Senior data science professionals play an active role in making crucial decisions using data insights.
They might also have to work with engineers to develop a more solid data architecture to improve the existing data pipelines.
Importance of Data Scientists in an Organization
By now, you’ve understood how important a Data Scientist is to accompany. Their main role is to make sense of the huge and complex sets of data developed every day. Instead of pulling out Excel sheets and logbooks, Data Scientists use predictive models to assist the company in using all this data and increasing their revenue.
Data Scientists are crucial to modern organizations for enhancing sales and marketing strategies. As these professionals help understand and interpret customer data efficiently, the existing products can be improved to serve them better.
New products can also be quickly developed. Data Scientists also take care of the data generated by various departments within the firm.
For example, Data Scientists can analyze the data produced by the Human Resource department to improve the hiring strategies and develop a more robust workforce.
Hiring a Data Scientist
To begin the hiring process of a Data Scientist, you first have to consider the following points:
- Does your company generate huge sets of structured and unstructured data
- Your company size – small, medium, or large-scale corporation
- Do you have multiple departments generating data?
- Are there Data Engineers to clean and manipulate these data sets?
- Do you feel the need to uncover data insights for business enhancement?
If you answered yes to most of these questions, you probably need a Data Scientist. If you have a large organization, then you might generate a lot of data. Data analysts and Data Engineers can’t handle all that alone. So, they will be properly assisted by a Data Scientist.
For a small or medium company, a single Data Scientist might handle all the data operations. However, the data infrastructure might be set up by a Data Engineer.
You must look for the following skills and qualifications in a potential Data Scientist:
- 5 to 7 years experience in the data science industry
- Good command over Microsoft Excel, SQL, Python, SAS, Tableau, etc.
- A master’s degree in computer science, mathematics, or statistics
- Project management experience
- Contribution in open source data science projects
- The ability to work individually and in a team
Who Is a Data Engineer?
The next most important position in any company’s data science department is that of a Data Engineer. A Data Engineer is responsible for preparing the infrastructure and foundation for data analysis. They are involved in developing systems for gathering, storing, processing, and analyzing data.
As organizations produce massive quantities of data every day, structured and unstructured, Data Engineers handle all the mess. They clean these data sets, remove unwanted and repetitive data to convert it into a usable form.
Therefore, all the raw data generated by internal organizational resources or external channels are sent to the Data Engineers.
They handle the production readiness, security, structuring, scaling, data manipulation, and transfer the data to the Data Scientists and Analysts for further operations.
They also have the responsibility of handling distributed systems for data analysis. The primary objective of any Data Engineer is to ease the data analysis process for other professionals and achieve business goals efficiently.
Roles and Responsibilities of a Data Engineer
Data Engineers develop data pipelines to transform the unstructured data into usable formats. They maintain the entire analytics infrastructure to support all data operations and functions within the organization.
They also have the responsibility of maintaining the databases, large-scale data processing systems, and servers. However, depending upon the company a Data Engineer works for, the job role and responsibilities may differ.
Generalist
This role is associated with small companies. In such a setting, Data Engineers might have to play multiple roles to handle the organizational data.
As the data science department is small, the Data Engineers have to collect, store, process, clean, and perform the final data analysis. However, the professional might not have to set up a data analysis architecture as small companies don’t generate massive amounts of data.
In most cases, Data Engineers in such settings have to do more work to handle user-centric data strategies.
Database-Centric
This role is associated with large multinational corporations that generate massive data sets and require much analysis. Data Engineers in these companies required solid knowledge about establishing data architectures.
Here, Data Engineers clean and prepare the unstructured raw data and send it to the analysts and Data Scientists. They also have to populate the analytics databases, work with pipelines and create table schemas. Also, they have to perform ETL – extract, transform and load – to move the data into the data warehouses.
They might have to oversee all the raw data stored in distributed systems and gather it in one location.
Pipeline-Centric
This role is related to mid-level companies that also have complicated data processing requirements. Pipeline-centric Data Engineers convert unstructured data into usable formats.
Data Engineers in such organizations constantly work to make sense of the collected data and enhance the analysis process. Therefore, they require in-depth knowledge of database systems such as Bigtable and Cassandra and distributed systems.
Other responsibilities of a Data Engineer include the following:
- Developing data pipelines according to business logic
- Determining inconsistencies in data that might affect the company’s objectives
- Develop, test, and maintain distributed systems and data architectures
- Improve data efficiency quality and reliability
- Create data set processes by using various Data Engineering tools
- Curate data for prescriptive and predictive modeling
- Manipulating, cleaning, and parsing data sets
- Preparing data for ETL processes
- Combining and sorting row data from various sources
- Examining data to facilitate task automation
- Developing data frameworks and identifying new data sources
Importance of Data Engineers in an Organization
As you have understood a lot more about the topic of “Data Scientist vs. Data Engineer,” you might have guessed the importance of a Data Engineer by now. Data science operations won’t start in a standard organizational setting until the Data Engineer processes the unstructured data.
Database Admins, Data Analysts, and Data Scientists are all somehow dependent upon a Data Engineer. It’s sent to other professionals only after the engineer converts the raw data into a usable format.
In some cases, Data Engineers are the first to be notified about what the company wants from the generated data. The data infrastructure requirements are also conveyed to them. Therefore, Data Engineers are vital while developing any business strategy influenced by data, especially in the initial stages.
Large corporations may have analysts and Data Scientists to handle the data, but Data Engineers can manage everything in a small business setting.
Hiring a Data Engineer
Before you move forward to hire a Data Engineer, you need to think about your company’s requirements.
If you have a large organization hiring a Data Engineer and a Data Scientist will be a good move. For a small or medium-level company, you can employ a Data Engineer to handle all your data-associated tasks such as collection and analysis.
To find the best candidate, look for the following skills:
- At least three years of experience in using data visualization tools, Python and SQL
- Solid knowledge in ETL processes and data warehouse platforms such as Amazon Redshift and Teradata Vantage
- Amazon Web Services, Linux, and Apache Hadoop skills
- Decent communication and presentation skills
- Ability to explain complex technical concepts to non-technical professionals lucidly
Data Scientist vs. Data Engineer: The Final Verdict
As you might have understood by now, the Data Scientist vs. Data Engineer debate can’t be settled so easily. It’s because both Data Scientists and Data Engineers are crucial to a company’s development.
As their responsibilities overlap, these professionals complement each other to achieve a common business goal. So, if you’re still wondering whether a Data Scientist or Data Engineer belongs to your team, you need to assess your business objectives.
You can stay assured of one thing though: whatever data-related target you want to achieve, these professionals will pave the way for you.
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