What Sets Data Scientists Apart from Data Engineers in the Tech Data Industry?

Over the years, the Data industry has experienced a lot of growth, and according to the Bureau of Labor Statistics, the demand for them will continue to increase in the coming years. While the roles of a data scientist and engineer are intertwined, and they both play an important part in transforming raw data into usable insight to make better decisions, their roles are different as they require different skillset. 

Kudoro Esther, a data analyst, gave a brief rundown of the role of Data scientists and engineers in the tech industry. 

“Data engineers build systems to aid the collection and storage of data. They build and maintain infrastructures for data collection, storage and processing, enabling data scientists to access and analyse data seamlessly.

While Data scientists analyse data to derive insights and build useful models, they access databases prepared by the data engineer, extract the required data, perform data cleaning, analyse the data, derive useful insight, create visuals, and report their findings to the stakeholders.

Using farming as an analogy, a data engineer goes to harvest farm produce (data) like tomatoes, peppers, and vegetables and takes it to the stalls in the market (databases), the data scientist is the chef who goes to the different stalls in the market and buy the farm produce (data extraction), cleans it (data cleaning), preps it (data preparation), plates and presents it for consumption (data visualisation and data reporting).

Data engineers use different tool sets, including MySQL, PostgreSQL, Hadoop, Apache Spark, Apache Kafka, Amazon Redshift, Google Cloud Platform, and Azure.

Data scientist tool sets, on the other hand, include Microsoft Excel, Python/R, TensorFlow, SQL, and Pandas”. 

While both roles are different, they work together seamlessly. Data engineers provide clean, accessible data that data scientists need to perform their analysis. In turn, data scientists analyse the data, identify trends and factors, and then use their models and insights to guide data engineers in optimising data storage and retrieval methods. Both of them are the backbone of any data-driven organisation.