Data Roles and Responsibilities

Data Roles and Responsibilities

In 2006, British mathematician and data science entrepreneur Clive Humby said, “Data is the new oil.” He was not wrong.

Employment of mathematical science occupations, which includes data roles, is projected to grow 27.9 percent from 2016 to 2026, much faster than the average for all occupations, resulting in about 50,400 new jobs. The main reason for the projected growth? Business and government use of "big data" relies on individuals in mathematical science occupations trained to process and analyze. (Bureau of Labor Statistics)

However, unlike oil, data cannot dry up or be completely tapped; it is infinitely available. The nascent nature of data coupled with its seemingly endless possibilities is paving the way for a whole host of new and evolving positions. A definition and explanation of the three most prominent types of data roles, data engineer, data scientist, and data analyst, follows:

Data Engineer

  • In essence, a data engineer is responsible for building data pipelines that transform raw, unstructured data into data ready to be analyzed. A data engineer is responsible for figuring out how to gather data, organize it and maintain it. It plays an incredibly vital role in the overall data team.

  • In addition to a robust set of technical skills, like a deep knowledge of SQL database design and multiple programming languages, a data engineer frequently works in between technical and non-technical stakeholders; equally as important to a data engineer’s technical repertoire are good soft skills. Often, data engineers will work with senior executives to determine what the company's data goals are.

Data Analyst

  • The data analyst dissects information to identify trends to enable decision-making. Typical responsibilities of a data analyst include performing analysis to understand the data, removing any corrupted data, preparing reports based on their analysis, and presenting findings to key stakeholders. 

  • Data scientist Harpreet Sahota can best describe the difference between a data scientist and a data analyst, the “data scientist discovers and [the] data analyst analyzes.” (Medium) Another way to distinguish between data roles is to consider whether the person acts before or after the data is collected. Data engineers, for instance, are responsible for operations before data is collected, while data analysts and data scientists are responsible for operations after the data is collected. (Chartio)

  • Data analysts are critical to a data team as they are responsible for mining through data and translating their findings to others on the team and a variety of other stakeholders. Data analysts are great communicators like data engineers and data scientists; they are exceptional at creating dashboards and other visualizations to communicate their findings effectively.

Data Scientist

  • Data scientists transform disparate data into clean, actionable insights. Data scientists combine key computer sciences skills with statistics and probability, mathematics, analysis, modeling, and business acumen to solve and answer an organization's most pressing questions around data. 

  • A data scientist concludes a data analyst's findings; in other words, a data analyst will report the facts about the data while a data scientist interprets the findings and makes recommendations. A data scientist is skilled at determining which results, usually reported on by the data analyst, are worth following up on; a data scientist creates focus. 

Like oil, raw data in and of itself is not valuable. Organizations beginning to work with data will require various data professionals to collect, organize and analyze their data, and give it value, i.e., turn it into actionable insights. While the three roles listed above are hardly the only data roles out there, and depending on the organization may be slightly different, the data profession centers around these three major data role types.

There are infinite possibilities in data careers at present, especially post-COVID-19, as organizations prioritize key digital transformation projects and work on fine-tuning their new and evolving customer journey, which is predominantly taking place on a digital platform of some kind. 

In many ways, the world of data is still in its infancy, and a lot of organizations are just getting their start in collecting, interpreting, and acting on their data. 

Are you a data professional? If you are, we would love to know you. Start your job search with Signature Consultants here.

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