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Data Engineer vs Data Scientist – Which One is Better?

The sphere of data science has seen an unprecedented surge in demand for well-versed professionals in recent years, demonstrating a noteworthy projection of 22% in job opportunities between 2020 and 2030, as per the Bureau of Labour Statistics findings. This surge significantly outpaces the average expansion noticed in many other occupations, highlighting data science’s vital role in modern industries. As firms intensify their emphasis on generating, accumulating, and scrutinising a pool of datasets to improve their operational efficiency, the escalating demand for data science expertise shows no signs of abating. 

 

In this all-inclusive guide, we delve into the nuances differentiating two prominent roles within the field of data science— data scientist and data engineer. Offering valuable insights into their respective responsibilities, average salaries, educational prerequisites, and diverse career pathways, this guide aims to equip you with the needed information to make an informed decision about the career that aligns best with your aspirations. Whether you aspire to ravel intricate data patterns as a data scientist or forge the way for efficient data systems as a data engineer, this write-up is your go-to resource for navigating the dynamic arena of data science careers. 

 

Table of Contents

 

Is there any difference between a Data Engineer and a Data Scientist?

 

There were days when Data Scientists were expected to fulfil the role of Data Engineer. However, the responsibilities have been categorised with the expansion and evolution of the data field, intricacies in data collection and management, and intensified expectations from organisations for profound insights. 

 

In this innovative domain, the key distinction lies in the fact that data engineers develop and uphold the frameworks as well as systems accountable for storing, extracting, and organising data. On the contrary, Data Scientists analyse a plethora of data to predict trends, extract in-depth business details, and address relevant questions for the firm.

 

Data Engineer vs Data Scientist

 

While there is some overlap in skill sets between Data Engineers and Data Scientists, and ironically, Data Scientists were required to handle data engineering tasks; these two roles are now distinctly separate. 

 

Roles and Responsibilities

 

It is advantageous to see data engineers and data scientists with complementary roles. Data Engineers build and improve the framework, encouraging and empowering Data Scientists in their tasks, whereas Data Scientists derive meaning from the enormous amount of data tackled by Data Engineers.

 

What Does a Data Engineer Do?

 

A data Engineer is an expert in preparing the data infrastructure for analysis. Their emphasis lies in the production readiness of raw data, encompassing elements such as formats, scaling, data storage, resilience, as well as security. Designing, building, testing, tackling, and optimising data from multitudes of sources are among the tasks allotted to data engineers. Additionally, they also develop the infrastructure and architectures facilitating data generation. The primary objective is to set up effortless data pipelines, leveraging an array of hefty data technologies for real-time analytics. In addition, data engineers formulate complex queries to guarantee easy access to data. So, this was all about what data engineers do.

 

What Does a Data Scientist Do?

 

Data scientists emphasise decoding novel insights within prepared data, usually handled by data engineers. Their responsibilities incorporate conducting experiments online, formulating hypotheses, and utilising expertise in statistics, data analytics, data visualisation, and machine learning algorithms to discern patterns and generate business forecasts.

 

In addition, Data Scientists collaborate with business leaders to understand specific requirements, covering intricate findings in both audio and video formats customised for a general business audience. 

 

Data Engineering vs Data Science- Education and Qualifications

 

Well, many individuals in Data Engineering and Data Science have a bachelor’s degree in Computer Science or a related area, for example- economics, statistics, mathematics, or information technology. While advanced degrees are often preferred by employers, securing a position in data science or data engineering is feasible with just a certification as well.

 

What Are the Prerequisites for Becoming a Data Engineer?

 

Typically originating from a software engineering background, Data Engineers are well-versed in programming languages like Python, SQL, Scala, and Java. Alternatively, they may hold a degree or certification in mathematics or statistics, allowing them to implement diverse analytical approaches to resolve business challenges. 

 

Firms generally seek candidates with a bachelor’s degree in applied math, computer science, or information technology to secure a position in data engineering. However, some companies may also need specific data engineering certifications, such as Integrated Program in Data Science, Artificial Intelligence & Machine Learning and more. Practical experience in constructing enormous data warehouses capable of executing Extract, Transform, and Load (ETL) operations on extensive datasets is also advantageous.

 

What Are the Prerequisites for Becoming a Data Scientist?

 

Data Scientists often witness extensive datasets that need a specified business problem to address. In such a manner, they are tasked with data exploration, pertinent question formations, and presenting their findings. Thusly, it is crucial for data scientists to have an all-inclusive understanding of multitudes of techniques in big data infrastructures, data mining, machine learning algorithms, and statistics. Staying up-to-date with the latest technologies is essential as they work with dynamic data sets, needing effective and efficient algorithm execution.

 

Proficiency in programming languages such as SQL, Python, R, and Java is expected from data scientists, with familiarity with tools such as Hive, Hadoop, Cassandra, and MongoDB.

 

Comparing Data Scientist and Data Engineer Salaries

 

Both data scientist and data engineer roles offer lucrative and gratifying career paths, especially for those inclined towards analytics. 

 

What is the average salary offered to Data Engineers?

 

Data engineers’ salaries are contingent on numerous factors, such as the role type, relevant experience, and location. According to the latest findings of Glassdoor, the average annual salary for a data engineer is approximately Rs. 8,95,000 per year.

 

What is the average salary offered to Data Scientists?

 

Similarly, data scientist salaries vary depending on skills, qualifications, location, and, of course, job type. On average, Glassdoor reports an average salary of Rs. 14 39 605 per year for Data Scientists. 

Navigating Career Paths

 

While there isn’t any fixed trajectory for becoming a Data Engineer or Data Scientist, individuals usually walk on certain paths to attain their desired roles.

 

Typical Career Path for a Data Engineer:

 

Data Engineering is usually not an entry-level position; hence, most of the data engineers commence with software engineering or business intelligence/systems analytics roles. These roles offer exposure to important systems and infrastructure in data science. 

 

On the other hand, Data Engineers may climb the ladder via positions like data architect, solutions architect, and database developer to hone and nurture their data engineering skills, deepen and polish their data processing and cloud computing, and gain experience with ETL as well as Data layers. Some may also switch from roles in data analytics to strengthen their knowledge of the needs of data analysts and data scientists prior to moving into data engineering.

 

Typical Career Path for a Data Scientist:

 

Regardless of internships or junior data scientists, many data scientists commence their careers from entry-level data science roles. These positions offer opportunities to build and polish technical skills and work on assigned projects prior to advancing to designing their experiments and handling extremely intricate business challenges. Data analysts often switch to data science roles, either by self-learning relevant data science skill sets or by participating in online certifications or boot camps.

 

Is switching roles from Data Scientists to Data Engineers possible?

 

Absolutely! Individuals can hop between roles with some additional advanced training. The shared skills, from programming languages to working with data pipelines, ease the career transition. Both data engineers and data scientists require acquiring additional skill sets pertinent to their new roles, contemplating the differing emphasis on infrastructure and hypothesis testing.

 

Which is beneficial- Data Engineer vs Data Scientist

 

Despite the overlapping skills, the unique responsibilities of Data Scientists and Data Engineers may resonate better with particular personality types. 

 

Consider Being a Data Engineer if…

 

Data engineers are adept coders who love and enjoy learning and adopting the latest technologies, relish finding advanced ways to improve software and system efficiency and prosper by contributing to organisational time and resource savings. If you enjoy tinkering, continuously seeking ways to enhance your creations, finding purpose in developing supportive tools, and experimenting with the latest advancements, a career in data engineering could be a perfect fit. 

 

Consider Being a Data Scientist if…

 

Data Scientists are analytical thinkers who are extremely inquisitive, fearless about asking questions, and revelatory in testing hypotheses. They not only utilise data to make sense of historical events but also forecast trends and seek to comprehend future possibilities. If, by chance, you find satisfaction in conducting advanced statistical analyses, developing machine learning algorithms, and employing creativity to solve problems, a career as a data scientist could be the ideal choice.

 

Build the Bridge to Data Nirvana

 

Data Science and Data Engineering are remoulding the IT sector, driving advancement, and promoting a culture of data-centric decision-making. As businesses recognise the transformative potential of these advancements, the demand for well-versed professionals is ready to see a surge, solidifying their key drivers of success in this tech-savvy era. So, what are you waiting for? If interested, Hero Vired is amongst the best online platforms offering certification in Data Science and Machine Learning in collaboration with MIT; join today!

 

 

FAQ's

It combines tools, technology, and methods to generate meaning from data. Modern organisations are inundated with data as there is a proliferation of devices that can automatically collect and store information.
Below are the lists of some skills employers usually look for:
  • Should be good at coding
  • Should have good problem-solving and analysis skills
  • Should be good with statistics and building testing and deploying models. 
A data scientist is a software developer ++ with stats and modelling skill sets to develop and deploy models and make inferences from data. 
Absolutely! In many Data Science interviews (ML Scientists, Data Scientists, ML engineers, and Data engineers), there are separate algorithms and coding rounds. Hence, preparing algorithms and data structures is a mandate. Some of the Analyst interviews, like a data analyst and a business analyst, might not have algorithms and data structure rounds. 
Data engineers must have a good knowledge of database management. This means being fluent with SQL (Structured Query Language), the basic language to interact with databases, and having skills with some of the most popular SQL dialects, which include MySQL, SQL Server, and PostgreSQL.
Data engineering is a rewarding career that is highly in demand and involves designing, building, and balancing data pipelines and frameworks. As a data engineer, you may come across many challenges and pressures, such as tight deadlines, complex problems, changing requirements, and high expectations.

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