Data analysts are responsible for a variety of tasks including data analysis, visualisation, munging, and processing of massive amounts of data. One of the most important skills of a data analyst is optimization. Here onse hould have the knowledge / skill to apply and modify algorithms that can be used to cull information from some of the biggest databases without corrupting the data.

Big Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized. Data engineers also update the existing systems with newer or upgraded versions of the current technologies to improve the efficiency of the databases.

The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries.

Machine learning engineers are in high demand today. Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc. machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.

Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/ disorganized” data to offer actionable insights which would help the companies in making better decisions.

A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures. They also ensure that the data engineers have the best tools and systems to work with.

A statistician, as the name suggests, has a sound understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.

The role of business analysts is slightly different than other data science jobs. While they do have a good understanding of how data-oriented technologies work and how to handle large volumes of data, they also separate the high-value data from the low-value data to identify how the Big Data can be linked to actionable business insights for business growth.

A data and analytics manager oversees the data science operations and assigns the duties to their team according to skills and expertise. Their strengths should include technologies like SAS, R, SQL, etc. and of course management.

Business analysts provide solutions that are often technology-based to enhance business processes. Organizations need these “information conduits” for a plethora of things such as gap analysis, requirements gathering, knowledge transfer to developers, defining scope using optimal solutions, test preparation, and software documentation.

Data storytelling is about finding the narrative that best describes the data and uses it to express it. A data storyteller needs to take on some data, simplify it, focus it on a specific aspect, analyze its behavior, and use his insights to create a compelling story that helps people better understand the data.