A quick search on any job portal will inform you that data scientists, data engineers, data analysts are some of the most in-demand jobs right now. But, there is a common perception among people not working in this industry that all of them do the same thing – analyse data and bring out meaningful information from it for businesses.
This is not true at all. A data engineer has completely different responsibilities in an organisation as compared to a data scientist, though some overlapping is possible. If you are starting out in the analytics domain and confused about what to go for—data engineering or data science—understanding the skillset and the outcomes required from both of them will help you make an informed decision.
A data scientist deep dives into the data and provides meaningful business insights from it that are crucial for decision-making in the company. They also work on building and deploying AI-based algorithms in various aspects of the business to solve business problems. Some data scientists also work on data visualisation and dashboarding mechanisms to spot trends and patterns within large sets of data.
Essentially, the data engineer builds the framework and structure for the data analytics pipeline for a business. These pipelines hold a very significant role for the business as they change the raw data into structures on which data scientists work. Data engineers also make sure there is a continuous flow of data from the servers to the applications and often collaborate with the data scientists in the organisation. They build new data analysis tools for the business analysts and also have the responsibility to ensure compliance with data security policies.
As data engineer builds the pipelines needed to analyse and work on data, they must have the following skills to successfully deliver results:
Database architecture and data warehousing – A data warehouse stores large quantities of data for analysis. This data is used for analytics, data mining and interpretation. A data engineer must be familiar with basic data warehousing concepts and the associated tools.
ETL tools – ETL (Extract, Transfer, Load) helps to extract data and transform it into a form that can be analysed. ETL tools help get data from different sources, change its format and store in the database for analytics professionals in the organisation to use. A data engineer must have a solid grip over these tools.
Knowledge of data structure – A data engineer is expected to have good knowledge of data structures as well. It can help understand the business goals of the organisation and deliver solutions based on that.
Programming knowledge – Knowledge of programming languages such as Python and Java is also a requisite to be a data engineer, with Python being the most in-demand. Strong coding skills help data engineers work on different programming languages a company uses in building its pipelines.
Though a highly-coveted job, a professional can only become a good data scientist if they possess the following skills:
Strong statistical and mathematical knowledge – Any company hiring for a data scientist will look for mathematical and statistical conceptual clarity. Even for building machine learning algorithms, the foundational concepts of statistics have to be top-notch. A data scientist should have solid knowledge of probability distributions, hypothesis testing, confidence intervals, etc.
Programming knowledge – A data scientist should also possess programming skills in languages such as R and Python, among others. Python has emerged as a popular choice these days for data scientists. These programming languages make it easier and quicker for data scientists to come out with insights from large datasets.
Business acumen – A data scientist works in an organisation where their analytical skills will help businesses make better decisions. It becomes fundamentally important for a data scientist to understand the needs of the business. They have to cater to the business problems the organisation faces and come out with solutions to improve them.
Whatever you choose, build genuine interest
While starting out or even transitioning from another profile, clarity on which area one wants to focus on in a company becomes crucial to build a solid career. An inherent interest and passion in the field can make the career journey even more exciting. Data scientists and data engineering roles are both highly rewarding, but one needs crystal clear theoretical knowledge and hands-on experience to build a successful career in them.
