A job description doesn’t always give a full picture when it comes to recruiting data roles. Most organisations aren’t clear on their exact requirements. This leads to many recruiters out there searching for unicorn candidates that don’t exist. It’s not their fault. The variety of job roles across the data industry has boomed in the last decade. As practitioners and professionals, we’ve done a really bad job at making things easy to understand.
What are the major differences when it comes to a…
A Data Scientist, Data Analyst, Research Scientist, Data Engineer, A Big Data Engineer, Machine Learning Engineer, Data Architect, AI Developer, DBA, BI Developer…
The list goes on. These are just some of the roles where the boundaries aren’t always clear.
Getting a consensus on the definitions and distinctions between them leads to huge debates in the online communities. We are constantly sent job descriptions on a daily basis. We have a huge responsibility to dig deep to clearly understand what a company’s requirement is when they’re looking to hire new staff.
A common confusion comes from understanding the difference between a Data Engineer and a Data Scientist.
Head of Data Engineering, Nick Partner says that: “As short as 5 years ago and, in some places, still, ‘data’ was a term. Now it isn’t uncommon to have a Data Engineering, Analytics Engineering, BI and Reporting, and some data science with maybe some MLOps thrown in for good measure too. Data engineering and data science are probably either ends of the value chain but neither more nor less valuable to an organisation. Data Engineers will often write scripts and do the plumbing to suck in data and put it maybe in a datalake or even raw storage and akin to software develops in tooling and mentality. Data scientists on the other hand are more akin to mathematicians using computational models to find hidden insight that can’t be seen by a trend line or bar chart from BI such as working out which customers are similar and what factors cause that to be the case.”
Director of Data Science at Matillion says it more poetically:
“Data engineers construct the sturdy foundations that deliver the smooth flow of data
Data scientists unravel hidden truths from that data to weave into decision-making tapestries.
Data engineers systematically turn those decisions into actions. Without data, there’s no insight, without insight decisions become guesses, without decisions action is directionless, without action insight is irrelevant.”
Here is our overview of the difference between the two….
A data engineer is responsible for the design, construction, and maintenance of the systems and infrastructure required to store, process, and analyze large volumes of data. They focus on building and optimizing data pipelines, data warehouses, and databases.
A data scientist, on the other hand, is responsible for extracting insights and knowledge from data through statistical analysis, machine learning, and predictive modeling. They possess strong analytical skills and a deep understanding of mathematical and statistical concepts.
Data engineers work closely with software engineers and database administrators to ensure data flows smoothly and is accessible to data scientists for analysis. They are proficient in programming languages such as Python or Java and have expertise in handling big data technologies like Hadoop, Spark, or SQL.
Data scientists work with raw data and use various tools and techniques to preprocess, clean, and transform it into a format suitable for analysis. They then apply statistical modeling, data visualization, and machine learning algorithms to uncover patterns, make predictions, and derive meaningful insights that can drive strategic decision-making. Proficiency in programming languages like Python or R, as well as knowledge of data analysis and machine learning libraries, is essential for data scientists.
So, there it is! If you have anything you would like to add or say on this topic, feel free to share your comments in the comments section below. If you have any questions, drop them in the comment section below, reach out to us on LinkedIn or drop us an email at info@altasu.com.