Data Professional Roles

Data is being generated at an unprecedented rate due to the increased use of the internet worldwide. There is an unprecedented demand to manage and utilize such vast amounts of data floating around the world. Today, data has replaced humans with driving crucial strategic business decisions in organizations. With the rising influence of data, demand for data professionals has skyrocketed in recent years. Companies have diverse data-related job roles where responsibilities across the roles have overlap functions. One of the main reasons for talent scarcity in such high-demand roles is the lack of clarity of the skills required for various data professional roles. In this article, we will look at different data job titles and how one differs from another.?

To help better understand some of the most in-demand data professional roles, let us iterate over the roles and responsibilities and understand the critical business functions of each role in detail.

Data Scientist

As a Data Scientist, you are responsible for designing, developing, and deploying data-intensive solutions for the organization and sharing your findings with stakeholders. To support the business solution, you'll need to prepare Big Data, develop suitable data models, and create databases. One should be equipped with good analytical skills complemented with a solid foundation in math and statistics for this position. Data interpretation necessitates critical thinking and problem-solving abilities.?

A Data Scientist must also have a solid grasp of business processes and narrative and data visualization abilities to communicate insights with colleagues throughout the firm. Although each function has separate data-related objectives and procedures, the skill sets necessary for these positions frequently overlap with other roles such as understanding software languages like Python/R programming, SQL, Tableau, and more.

Key responsibilities of a data scientist are:

• Collaborate with stakeholders across the organization to find opportunities to use corporate data to create business solutions.
• Information from company databases is mined and analyzed to promote optimization and enhancement of product development, marketing tactics, and strategic plans.
• Increase and improve customer experiences, revenue creation, ad targeting, and other company results.

Data Analyst

The data analyst acts as a gatekeeper for an organization's data, ensuring that stakeholders understand it and utilize it to make intelligent business choices. Most freshers interested in a data-related career begin as data analysts. All you need is a bachelor's degree and a solid understanding of statistics. Strong technical abilities would be a bonus and would put you ahead of the majority of other applicants. The ideal candidate for this job should also be skilled in programming, machine learning, and data visualization. Analysts frequently work with massive data sets and must have excellent mathematical abilities. Data analysts must also have a firm grasp of programming languages.

Data visualization is an essential element for data analysts since it portrays data to allow company executives to make quick decisions focused on day-to-day operations. This function of data storytelling overlaps with the role of a data scientist.

Key responsibilities of a data analyst are:

• Designing and managing data systems and databases, including addressing code level and other data-related issues.
• Using statistical techniques to evaluate data sets, focusing on trends and patterns that might be useful for diagnostic and predictive analytics initiatives.
• Data mining from primary and secondary sources, followed by data reorganization so that both humans and machines can readily read.

Data Engineer

Data engineers set the groundwork for a database's design. They evaluate a wide variety of needs and use suitable database methods to build a robust architecture. A data engineer manages the database and assures that it operates correctly and without interruption. If you wish to be recruited as a data engineer, a Bachelor's degree program in Computer Science, Mathematics, or any other IT-related field is necessary. Additionally, professional certifications might be beneficial to boost your chances. You should be familiar with database systems and data warehousing.

Similarly, you should be able to do a data storage comparison study. Understand the differences between relational and non-relational database architectures. It requires knowledge of SQL, NoSQL, and other advanced data querying languages.

Key responsibilities of a data engineer are:

• Identify, develop, and execute internal process enhancements such as improving data distribution, automating manual procedures, and re-designing architecture for improved scalability.
• Create and maintain the data pipeline architecture for extraction, transformation, and collection of data from a wide range of sources by leveraging SQL and cloud technology.
• Create data tools to aid analytics and data scientist coworkers to construct and optimize our business to become an innovative market leader.

Data Architect

Data architects are senior strategists who convert business objectives into technical specifications and create data standards and principles. The data architect is in charge of conceptualizing and developing the data pipeline architecture for a company. There is no standardized accreditation for data architects. Typically, data architects begin their careers as data analysts, data engineers, or solutions architects and progress through the ranks to become data architects with years of experience and expertise in data design, information management, and data storage. Most data architects have degrees in information systems, computer science, or a closely related discipline.

Key responsibilities of a data architect are:

• Business needs are translated into technical specifications, including data streams, integrations, transformations, databases, and data warehouses.
• Defining the architectural data framework, norms, and principles, including modeling, metadata, security, reference data like product codes and client categories, and master data like clients, vendors, materials, and personnel.
• Collaboration and synchronization with a range of disciplines, stakeholders, clients, and external stakeholders

This article introduced you to four critical data professional positions on a data team: data engineer, data analyst, data scientist, and data architect. All positions have several overlap functions, and a data professional should not work in isolation. Every member of the data team should operate following business needs and accept responsibility for their roles.