As a big data enthusiast, you’re stuck at professional crossroads. Should you stay in your current and familiar job role or should you make a career transition on a completely novel career path?  Considering the growing demand and skyrocketing growth for data engineers and data scientists, you decide to make a career transition. Now you’re confused whether you are better suited to become a data engineer or a data scientist. More specifically, how do you choose between becoming a data engineer vs. data scientist?  Both data job roles would appeal to your affinity for problem-solving and let you exploit your love for big data. Just similar to a data scientist, a data engineer also works with big data. Data engineers and data scientists are the two most recurring job roles in the big data industry that require different skillsets and focuses. Data engineers and data scientists both share a common goal – helping organisations leverage data for better decision making. However, the methods they use to handle data and their use cases are totally different. Data Engineer vs Data Scientist – there is a great deal of confusion surrounding the two job roles.

In many start-ups or smaller organisations, a data scientist is also donned with the hat of a data engineer for the sake of cost savings and efficiency. However, larger established companies typically employ both data engineers and data scientists to perform unique tasks, making the differences between the two job roles important to understand. Before making a career-defining choice, we suggest you should know the differences between these two job roles. To help clear the confusion, in this article we’re going to define each role, look at the skills needed for each, and tear down the day-to-day responsibilities of each. 

Data Engineer vs Data Scientist – Understanding the Difference

First, let’s start with some basic definitions of each role on who does what.

What is a Data Engineer?

A data engineer is a “specialised software engineer by trade” who is database-centric and pipeline-centric. He creates optimised data pipelines and manages tables and datasets to be used by data scientists and analysts. Data engineers use ETL techniques to collect relevant data, transform it and load it into the data warehouse that can be used by anyone(analyst or a scientist) in the data science team for developing end-to-end business solutions. They also build algorithms to provide ease of access to raw data to data scientists and analysts. The main focus of a data engineer is to build optimised software solutions and maintain the architecture of the database for data generation. A data engineer cleans the data to transform it into a usable format for data scientists. A data engineer’s effort on building robust engineering systems is what helps data scientists analyse data efficiently. 

What is a Data Scientist?

A data scientist is a superhero with a unique blend of skills who trains machine learning models or performs advanced statistical analyses to make predictions and provide metrics to solve business problems. A great data scientist is similar to a master chef who knows that a delicious and best dish can be prepared only by using high-quality ingredients. Therefore, a data scientist relies on a data engineer to have access to high-quality data that can be fed to machine learning models, analytic programs, or other statistical methods to draw inferences. 

Data scientists work with data that has already passed one round of cleaning and manipulation by a data engineer. A data scientist further cleans and prepares data so that it is ideal for feeding into a machine learning model to identify trends and find patterns in the data to predict the future. Data scientists then communicate these findings to various business stakeholders through a compelling story and stunning visualisations for data-driven decision making. A data scientist is not involved in developing or maintaining a data architecture like data engineers.

Data Engineer vs Scientist – What do They do?

‘A scientist can discover a new star, but he cannot make one. He would have to ask an engineer to do it for him.’ — Gordon Lindsay Glegg

A data engineer is responsible for developing, testing, and maintaining data architectures while a data scientist cleans, processes, and organises data for advanced analytics. A data engineer cleans the data to rectify any human or machine errors like mismatching formats, data types, invalid inputs, or system-specific codes while a data scientist cleans the data to make it usable for feeding to machine learning models and statistical methods and avoid any errors that could be problematic during analysis. 

Data Engineer Data Scientist
Create and maintain databases, data pipelines, and data architectures. Use various techniques in statistics, machine learning, advanced analytics, and big data infrastructures to analyse data from a novel business perspective to identify hidden trends and opportunities.
Build data architectures to support the needs of data analysts and data scientists. Develop reliable and efficient operational models and data-driven solutions for pressing business challenges. 
Explore various methods to improve data reliability, scalability, and quality. Prepare data for prescriptive and predictive modeling.
Build complex database queries to create data pipelines. Conduct research to answer business queries.
Identify various novel methods for data acquisition.  Take initiatives in adapting novel data science approaches for the business.
Ensure optimised performance through continuous monitoring and testing. Improve data collection strategies to include information that is needed for the development of analytic systems.
Integrate new datasets into existing data pipelines. Create and automate various machine learning tools for the organisation.
Create various data modeling and data mining processes. Communicate the analytic findings to stakeholders through visualisations and stories to help businesses realise hidden revenue streams.

The below data scientist and data engineer job descriptions clearly explain the difference between the day-to-day tasks involved in these job roles –

Data Scientist Job Description
Data Scientist Job Description

Data Engineer Job Description
Data Engineer Job Description

Required Skills for Both the Job Roles

There is a huge overlap of skills between a data scientist and a data engineer on programming, analysis, and big-based technologies. However, the overlap of skills varies in the expertise level each one has. For example, the analytic skills of a data scientist are far more advanced than that of a data engineer while the coding skills of a data engineer are way beyond the coding skills of a data scientist. A software engineering background is a must-have for data engineers while it is beneficial if a data scientist has a software engineering background, however, it is not a mandate. The primary knowledge a data scientist must have is expertise in math/statistics and programming unlike a data engineer who must have expertise in data warehousing, data architecture, and ETL tools and big data analytic tools like Hadoop, Spark, Pig, Hive, HBase, etc. 

Data Scientist Toolkit Data Engineer Toolkit
Math, Probability, and Statistics Expertise in big data based technologies like Hadoop, Spark, HBase, Pig, Hive, etc.
Expertise in programming languages -Python and R  In-depth knowledge of SQL and NoSQL databases like PostgreSQL, MySQL, MongoDB, Cassandra.
Familiarity working with tools like RapidMiner, Weka, KNIME, TensorFlow Data Modelling 
Knowledge of SQL and NoSQL database technologies. Familiarity with programming languages like Java, Python, C++, etc
Data Wrangling Knowledge of various operating systems like  Solaris, UNIX, or Linux is helpful.
Machine Learning Data warehouse architecture, Software Engineering,  and ETL solutions.
Data Visualisation and Communication Data Visualisation and Communication

Data Engineer vs Data Scientist – How Much do They Earn?

The big question: money. It’s no secret that both data engineer and data scientist are top emerging and most in-demand analytic job roles with impressive salaries. A data engineer might not garner the same amount of media attention as that of a data scientist but earns as much as a data scientist.  According to PayScale, the average entry-level annual salary for anyone starting out as a data engineer or a data scientist is 8 lakhs. However, the average experienced data scientist salary 18 lakhs and can go as high as 30 to 40 lakhs based on years of experience, length of time working with specific data science tools, location, and skills required for the job. Experienced data engineers earn an average annual salary of 16 lakhs and this can go up to 30 lakhs. Data engineer and data scientist salaries are dependent on the job description.  So, to earn a handsome salary either as a data scientist or as a data engineer, you have to match the employer expectation on experience and skillset. 

Average Data Scientist Salary
Source: Payscale

Average Experience DS Salary
Source: Payscale

Average Data Engineer Salary
Source: Payscale

Average Experience DE Salary
Source: Payscale

Data Engineer + Data Scientist – A Perfect Match Made In Big Data

Data engineers lay the groundwork and bring speed to a data scientist’s job.

You can think of data scientists as the lead roles in a blockbuster movie while data engineers and data analysts are the supporting cast in the movie contributing to the overall box office success. Just like ostriches and zebras travel together in nature to survive, data engineers and data scientists have a symbiotic relationship in working collectively to build an amazing data product.  These data job roles, in fact, complement each other: a data engineer uses his programming and software engineering skills to design, build, and maintain data pipelines. Data engineers use their architectural, programming, and system design skills to clean the data (remove missing values, mismatched data types, and other data quality issues) which can be used by data scientists to build and analyse machine earning models that add value to the business. A data scientist cannot glean meaningful business insights without access to large volumes of data and it’s the data engineers that provide them the tools and access to quality data to get their jobs done.

Let’s say the manager of an e-commerce company likes to figure out how to reduce the logistics cost involved in product returns. Considering a big data perspective, there are a couple of things that should occur. A data scientist has to figure out what factors drive returns for a particular category of products. This is the question a data scientist would be trying to find the answer for. Based on the conclusions they arrive at after the analysis they will work with other stakeholders to develop return policies and metrics that can reduce the logistics cost involved in the return. Data engineers will move the data from the POS or other data sources into the data warehouse using the ETL technique, clean the customer data on returns the company has, and create tables to help data scientists answer the question. Data engineers will also develop analytical tables to help track past and future metrics for product returns. How the product return metrics are created would purely depend on the insights a data scientist gleans from the data provided by data engineers. 

A strong collaboration between a data engineer and a data scientist not only helps organisations develop better data products but also is beneficial in making the workflows better. It’s the smooth partnership between a data engineer and data scientist that helps companies add value to data and both are important for a data science team to function productively. Lumping together the role of a data engineer and data scientist results in a drastic impact on system performance, efficiency of the data science team, scalability, and deploying new analytic models into production.

How to Prepare for Both the Roles?

Now that you know the difference between a data engineer vs data scientist, you might want to learn more. Having understood the importance to build these skills, it’s also important to find a comprehensive program that helps you upskill and put you on the path towards success as a data engineer or a data scientist. If you’re thinking about transitioning to a data engineer or a data scientist position, consider mastering foundations of data science and data engineering from Springboard. At Springboard, we have comprehensive upskilling career paths available for professionals interested in pursuing data scientist, data engineer, or data analyst job roles. Springboard’s 1:1 mentor-led 6-month online data science program focuses on building both technical skills and soft skills such as communication, management, and organisation skills that are beneficial for both data engineer and data science job roles. Plus,  it’s the only data science program that comes with a job-guarantee and helps you advance to high-paying in-demand analytics job roles. Graduates of the data science program build an impressive data science portfolio of real-world projects demonstrating their skills and expertise, an asset that helps them stand out in their data science interviews.