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The Correlation Between a Data Scientist and a Big Data Engineer

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Big data is on the verge to take center stage.

With over 44 zettabytes of data generated at the beginning of 2020, data is predicted to grow up to approximately 463 exabytes by 2025, (Source: Seagate UK). The rate at which data is accelerating over the past years is at a more-or-less predictable rate. Therefore, offering opportunities to companies to rapidly improve their products or services. As such, those companies have started realizing the significance of data scientists and big data engineers.

However, most people are still confused between a data scientist and a big data engineer.

As the data space started extending, so did the job roles in the field. Big data engineers were created as a separate entity because of the unique skills the role demands.

Both offer unique skills and expertise to companies in terms of providing business value. As we proceed further, we will dwell deeper and understand the individual job roles and responsibilities, and how they’re different.

Big Data Engineer Vs. Data Scientist

Picture this scenario.

A data team is asked to build a model to predict customer churn rate. What are the factors you need to address before building the model?

Don’t you think the team needs to first analyze what type of algorithms will be suitable for the model or the type of evaluation framework required?

Most importantly, ensuring the data pipeline is well-defined or the data gathered has been properly analyzed. Since the model will go into production codebase, you need to ensure your code is clean. Not to mention, before any type of analysis, the customer data information needs to be correct. And this includes undergoing multiple steps such as cleaning the data and structuring raw data using data science techniques.

Now here’s the catch – data scientists design the framework and glean data whereas big data engineers implement and build the model based on the design.

So, here’s how both the job roles are defined.

Big data engineer

Design-Build -Arrange - data pipelines.

Before the emergence of big data, businesses had to do their analyses and use intuitions while making business decisions. However, to resume this in an orderly manner, we now use modern technologies. Big data now offers manifold advantages creating better businesses. However, if you need to take advantage of big data, you need to use the right set of tools and most importantly the right type of people to manage data. Additionally, it also depends on the type of tasks you want these professionals to perform – data engineers or data scientists.

A big data engineer is responsible for managing the organization’s big data infrastructure and tools. As such wherein they need to develop, evaluate, maintain, test big data solutions within the organization. At times, they also get themselves involved in designing big data solutions with their vast expertise in Hadoop based technologies like MapReduce, Hive MongoDB, and Cassandra. They hold expertise in building large-scale data processing systems and require to have hands-on experience in data warehousing solutions.

Before making a move into the big data career, the individual needs to have experience in software engineering. Additionally, the candidate needs to have extensive experience in programming, testing patterns, and an object-oriented design.

Data scientist

Analyze -Test -Create -Present -the data.

As a data science professional, the individual is responsible for analyzing, testing, and optimizing data and present it to the company in a way business stakeholders and senior management understands.

Expert professionals from this background need to have in-depth knowledge of mathematics and statistics. These subjects hold the foundation of a data science career. Without this knowledge, the individual won’t be able to create machine learning nor artificial intelligence models.

The typical task of a data scientist in an organization mostly involves cleaning the data, finding solutions with the data gathered, communicate with the team, solve complex problems.

All in all, data scientists need to be good with communication skills, thus making it easy for them to communicate and present the data to the business stakeholders.

If you’re wondering about the salary compensation, both data scientists and big data engineers earn lucrative compensation. A data scientist makes near about USD139,000 and a Big data engineer around USD 142,000 per annum, according to Glassdoor.

As the demand keeps increasing the compensation will also increase for skilled talents in the industry.

In conclusion

Now that you’re aware of both the job roles, you can choose anyone that best fits your criteria. Despite the career path you choose, you will still need to equip yourself with basic skills such as programming, mathematics, and statistics.

That said, most employers have started taking heed of online credentialing such as certification programs.

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