Data volume and variety is ever-increasing in organizations. There are various job roles that are available in the data space and as data volume increases, demand for many of these roles are increasing too. Let us talk about 3 specific job roles and the skill required for them in this article.
The Job roles :
Data Analyst – Data analyst is typically an entry-level job where data is mined and analyzed to get insights for the business function, department or the business process. Data analysts need to have some understanding of the overall business or the function for which they are working in so that they understand the corresponding data. Business metrics or KPI are created in dashboards as part of standard reporting process and also ad-hoc reports are created based on insights by the data analysts. These are analyzed by them and shared with functional leaders or management team to provide a detailed look at business performance.
Data Scientist – Data scientist is a senior-level position. Based on the findings of reports (created by data analysts), they drive specific data initiatives in the business function. Data scientists work with statistical model or machine learning algorithms to forecast business directions and optimize resource performance. They may apply NLP or advanced deep learning models, based on the business use case, and build models to test certain hypothesis and evaluate its performance in the context of business. They work in close association with data analysts and the business teams to understand the business scenarios and the data in the context of the scenario. They also work closely with data engineering team that in turn works closely with Devops teams for deployment of models in production.
Data Engineer – Data engineers source data from different data sources and prepare data pipelines from data sources to data warehouse / data store / data lake. They carry out data transformation and data loading and, prepare the data for use by data analysts and data scientists. They also work closely with data architecture and database teams of the organization. As organizations are using different IT and business applications it is giving rise to different types of data. So data engineers need to work with different sources of data as well as different types of databases for creating data pipelines and make the data available for data analysts and data scientists to work on.
The other job roles in the data science field is covered in article “Careers That You Can Have With Data Science”
https://databrio.com/blog/Careers-That-You-Can-Have-With-Data-Science-part-2
The skillset required :
Data Analyst – Typically data analysts need to possess analytical and story-telling skills with data. They also need hard skills in tools like Excel, visualization tools like PowerBI, Tableau to create reports and dashboards. Programming skills in python and R helps them to work on data transformation layer and also work with upstream or downstream consumption/integration of data. Exposure to SQL (and its variations like MySQL, MSSQL server) is also helpful to query and work on data. As the data and metrics belong to the business, data analysts need to have an understanding of the business as well in order to build and tell the story.
Data Scientist – Data Scientists need exposure to many skills ! First and foremost is the understanding of the business and the data. As data scientists build models to address business problems, they need to have good expertise on statistical methods, machine learning and deep learning models. They need strong skill on programming language like python, R, Julia etc. for building these models and compare performance. Some exposure to SQL and dashboarding tools like PowerBI/ Tableau help them query data quickly. Data Scientists work closely with data engineers and data analysts in the team and also interact frequently with the business team to check the performance of the models and fine-tune those.
Data Engineer – As data engineers work in extracting data and data preparation, they need to have strong expertise in databases – SQL and big data – as well as ETL (Extraction, Transformation, Loading) or ELT tools like Talend, Informatica, Alteryx or similar open source tools. Programming language skills in python or Java is needed for creating data pipelines and transformation.
The opportunity and salary in these job roles:
As organizations are embracing digital transformation, there is higher need of data analysts and data engineers in the near future. Once data pipelines are created, reports and dashboards are developed, data scientists come on board to improve topline and bottomline of business in conjunction with the functional teams. The companies that are using data science to stay ahead of their competitors, use the services of data scientists to design pricing strategy, supply chain strategy and also to develop new products and services for the growth of the businesses. As data adoption is increasing, the need for data savvy engineers, analysts, managers and data scientists are also on the rise.
In terms of salary, the average salary of data scientists is more than that of data engineers which is more than data analysts.
The average salary for Data engineers in india is 10.76 Lakhs per annum as reported by Indeed https://in.indeed.com/career/data-engineer/salaries.
The average salary for data analyst grade 1 to grade 3 ranges from 4.83 lakhs to 7.5 lakhs per annum as reported by Indeed. https://in.indeed.com/career/data-analyst/salaries There is also variation in salary reported based on location/city of job in spite of the flexibility of Work-from-home policy.
An overlap of skill and activities are seen between these job roles in the industry, hence, with exposure and experience, a data engineer who has keen interest in quantitative techniques can graduate to senior data analyst role and a data analyst who has keen interest in data architecture and data systems can grow into the role of a data engineer.
