Data Scientist vs Data Engineer 2024 – A Step-by-Step Hiring Guide

Table of Contents

Introduction

Data is the new oil of the digital economy – an untapped, immensely valuable asset. We live in a world that creates about 2.5 quintillions of data every day. We live in a world where global enterprises are urgently implementing data science and analytics strategies to improve business performance. And we live in the same world, where businesses are still comparing data scientist vs data engineer.

A few years back, some presumed that by the end of 2018 the IT industry would face a severe talent shortage for data scientists. It will become harder to keep pace with the rising demand for expert data scientists. Besides, one more assumption pushed data scientists on the backfoot that every other thing in data science will be automated by 2030. However, what we noticed is despite all presumptions, assumptions, and disruptions, the demand for data scientists is still growing. Let’s explore which one is an ideal choice, data scientist or data engineer.

What is a Data Scientist?

A data scientist is a professional responsible for analyzing and processing large sets of structured and unstructured data. A data scientist possesses excellent skills in computer science, statistics, and mathematical applications. They analyze, model data, design frameworks, and then utilize their skills to interpret the results extracted from the data so that companies or organizations can create actionable plans. As IBM explained, who is a data scientist, the one “partly analysts and partly creative.”

What is a Data Engineer?

A data engineer manages and optimizes data infrastructure for data collection, management, and transformation. A data engineer creates pipelines that convert raw data into forms usable by data scientists and other consumers. They integrate, consolidate, cleanse, and structure data for analytics applications. If you are still wondering who is a data engineer then justifying in layman’s terms the one who aims to make the data accessible and optimize the organization’s big data ecosystem.

Major Difference Between Data Engineer And Data Scientist

Data scientists and data engineers play a crucial role in data utilization and analytics, and their roles guide the different aspects of exploiting this valuable resource.

Suppose you want to invest in data analysis and build a team to implement a data-centric culture. In that case, it is a must for you to understand the differences between Data Scientist vs Data Engineer 2024. Knowing this difference will help you hire data engineers or data scientists per your needs and utilize their skills to meet your objectives.

The major difference between data scientist and data engineer is that data engineers focus on building and maintaining the frameworks and structures that can fetch and store data in an organized manner. On the other hand, data scientists focus on analyzing the data to identify trends and extract useful insights to assist organizations in making decisions to increase profitability and productivity.

Data Scientist Duties and Responsibilities

Research and discovery is one of the core data scientist duties, and a data scientist does the same. The research data discover patterns, trends, and information invisible to the human mind and eyes. The data scientists discovered information or data insights help businesses make better decisions, streamline business processes, optimize operations, and increase ROI.

The responsibilities of a data scientist are depending on the needs of an organization. However, a summary of several responsibilities they fulfill is mentioned below,

• Gather data by identifying different inside and outside sources.
• Process and clean the data to make it ready for modeling and discovery.
• Find the right questions to begin the discovery and analyzing process on structured and unstructured data.
• Understand business challenges, and collaborate with the team to create data strategies and design solutions.
• Identify and utilize the precise algorithm and module to process and analyze the data.
• Use appropriate machine learning, artificial intelligence, data science, and statistical techniques to uncover the trends and patterns in data.
• Explore additional technologies and tools necessary to explore, analyze, and visualize data insights.
• Customize analytics solutions using various tools, applied statistics, and ML algorithms.
• Present the analytical findings to the business leaders using various data visualization tools.
• Update the solutions or analytics process based on the feedback received.

Data Engineer Roles and Responsibilities

The primary focus of a data engineer is to create free-flowing data pipelines using a combination of big data technologies and tools. As the name suggests, data engineers build, test, and maintain data architecture so data analysts and scientists can use the data in real-time to extract value-based insights.

The raw data collected for analysis contains a lot of anomalies and all sorts of errors. Such data is worthless for data scientists. To make the data usable, data engineer creates reliable data pipelines that interconnect data from different sources and transfer it from one format to another.

Here is a summary explaining data engineer responsibilities,

• Collect data from different sources as it is, where it is mannered.
• Design, develop, build, test, and maintain data architectures and processing workflows.
• Build robust, comprehensive, reliable, and efficient data pipelines.
  Understand the data requirements and create solutions for comprehensive data acquisition.
• Ensure the data architecture they build supports business requirements and integrates with their data science strategy.
• Develop datasets to be used in data modeling, mining, and production.
• Enhance collection of new data and refine existing data sources.
• Work on different ways to enhance data quality, reliability, and efficiency.

Skills of a Data Scientist

As discussed earlier, data scientists need to be a pro in mathematics, statistics, and machine learning techniques. Their job role majorly revolves around combing the best of models, architectures, algorithms, and tools to get the job done.

Here is a list of skills a data scientist has,

• Mathematics and Statistics
A data scientist has a computer science background and a strong foundation in maths, stats, and probability. Knowing mathematics and statistics is the primary requirement to become a data scientist. Creating hypotheses, models, and flows to work on different machine learning algorithms constitute the foundational skills of a data scientist.

• Machine Learning
Data science works on a core principle of extracting knowledge or information from the data. Therefore, basic familiarity with machine learning models and algorithms is another skill set every data scientist has.

• Programming Knowledge

A data scientist must be well versed in programming languages like R Python. Besides, they must have coding skills to build databases, software development lifecycles, and analytic solutions meeting business needs. Almost all data scientists have proven skills in using data science tools and technologies.

• Data Visualization

Having a strong hold on data analysis and visualization is a major skill set for data scientists. An ability to look beyond patterns, trends, and KPIs and a strong understanding of various data analytics and visualization tools help them transform data into insights and present them in a visually appealing format.

• Managing Database

Profound knowledge of databases and managing data is the foremost skill of a data scientist. Managing large databases, cleaning, processing, modeling, structuring, and processing data is their core responsibility. Hence managing large databases with expertise in different data storage domains such as MongoDB, PostgreSQL, MySQL, Open Source NoSQL Database, Databricks, AWS, Casandra, Oracle, etc., is a must.

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Skills of a Data Engineer

As discussed earlier, data scientists need to be a pro in mathematics, statistics, and machine learning techniques. Their job role majorly revolves around combining the best of models, architectures, algorithms, and tools to get the job done.

Here is a list of skills a data engineer has,

• Database Systems
A data engineer has excellent knowledge of managing rational databases and standard programming languages like SQL and NoSQL. They are excellent at manipulating database management systems (DBMS) – a software application offering an interface to databases for information storage and retrieval.

• Data Warehousing Solutions
Data engineers possess exceptional knowledge of data warehousing. Hands-on experience in Amazon Web Service and Microsoft Azure is an essential and fundamental skill set for a data engineer. Besides, creating data warehousing solutions and customizing the existing solutions is a necessary skill set for data engineers.

• ETL Tools
ETL stands for Extract, Transfer, and Load. It is an important aspect of data science requiring data engineers to have profound knowledge of data pulling, batch processing, applying rules to specific data, and then loading transformed data into databases for further viewing or processing. A data engineer is well versed with almost all ETL tools used in the process to get the job done.

• Data APIs
A data engineer must be a nerd using the Application Programming Interface (API). Knowledge of APIs is a prerequisite for data integration, processing, or any activities related to a data engineering job. APIs offer a bridge to connect various applications and data sources and transport their data. Data engineers predominantly rely on REST APIs. Also called Representation State or REST APIs provide seamless communication over HTTP, establishing them as a valuable asset for any web-based tool.

• Programming Languages
A data engineer must have exceptional skills in versatile programming languages, especially backend and query languages, considered specialized languages for statistical computing. Python, Ruby, Java, and C# are some of data engineers’ widely used programming languages besides SQL and R.

Data Scientists Vs Data Engineers Tools: Best In 2024

Due to the availability of numerous tools for Data Science and Data Engineering, being confused to choose the best tool is not an easy task. Here is the list of tools that data scientists and data engineers consider the best in 2024.

Data Science Tools 2024

Data Science has become incredibly popular in the 21st century. Companies hire Data Scientists to understand their customers better and improve their products. A data scientist must have hands-on experience in various tools and programming languages. Let’sLet’s look at some of the popular data science tools used in 2024.
1. SAS
2. Apache Spark
3. BigML
4. D3.js
5. MATLAB
6. Excel
7. ggplot2
8. Jupyter
9. Matplotlib
10. NLTK
11. TensorFlow
12. WEKA

Data Engineering Tools 2024

To offer excellent Data Engineering Services our Data Engineers build data pipelines and help design data infrastructure. They also work on algorithm development, making data more useful to companies. To build a rich data infrastructure, data engineers require a mix of programming languages, data management tools, and other tools for processing and analyzing data. Here is a list of top tools and technologies used by data engineers in 2024.
1. Python
2. Snowflake
3. Amazon Redshift
4. Hevo Data
5. Google BigQuery
6. Fivetran
7. SQL
8. PostgreSQL
9. MongoDB
10. Tableau

When to Hire a Data Scientist?

• When you need analytical thinkers who aren’t afraid of asking questions, think about hiring a data scientist. These professionals are dedicated to take any efforts necessary to test their hypothesis.
• Prefer hiring a data scientist when you want the data to make sense when you want to forecast the trends by analyzing the things that happened in the past and need to understand the probability of what might happen in the future.
• It’s better to onboard data scientists when you want advanced analytics, write machine learning algorithms, and use AI and deep learning models.
• Hire a data scientist when you want to analyze the data statistically, find patterns, understand the relationships between variables, and offer visualizations of the insights to the decision-makers.

When to Hire a Data Engineer?

• Hiring data engineers is the best choice when you need someone to manipulate, transform, and cleanse the raw data that data scientists can use for analysis and building machine learning models.
• Data engineers are excellent at preparing or working with infrastructure and architecture that stores the organizational data and moves it and the code driving it. They also ensure the data is equally accessible by all stakeholders within the organization.
• Hire data engineer when you want someone to design, build, test, integrate, manage and optimize data from various sources.

How to Hire Data Scientists?

Hiring data scientists can be tough. Companies of every size and industry need the insights that data science can provide. Data scientists use statistics and computer science to turn raw data into actionable information.

While you’re fighting against the fierce competition for limited qualified candidates and trying to ensure that your hire will fit into your organization, traditional hiring methods may not work for you.

The list of data science technologies is constantly evolving. The popularity of languages and tools changes yearly, and new frameworks are constantly being developed. Before starting the hiring process, it is good to research the unique requirements for your role to understand when and how to hire data scientists.

Hire According to Your Needs - Freelance or Full-Time

Companies looking out to hire a data scientist do have options to hire them as freelancers or as dedicated depending on their budget and requirements. Data scientists work as consultants on an hourly basis and monthly basis.

When requirements are specific, and projects are short, companies can hire data scientists as freelancers. During the tenure, the company can utilize the skills and expertise of the data scientist to analyze its data and draw conclusions that it can use to improve its business practices.

When the company requires someone specialized to monitor their data constantly and add value by providing actionable insights regularly, companies can hire data scientists full-time. Hiring full-time data scientist can be cost-effective because the company will have an expert professional onboard who can dedicate 40 hours of the week to working on their project.

Look beyond the resume.

Data scientists are uniquely qualified to solve complex problems and communicate with non-technical professionals. New hires need a wide range of skills to succeed in this role, which isn’t easy to represent on a resume. Your ability to assess these four key competencies will help you identify the best candidates and make better hires.

• Problem-Solving – The key to problem-solving is breaking it down into smaller, more manageable parts, then recombining those parts into a solution. Data scientists use this approach when designing algorithms for computers. By breaking down a problem into smaller pieces, they can figure out how to solve it and translate those steps into instructions that a computer can follow.
• Technical Communication – Technical communication means making complex information clear, concise, and understandable—for example, by translating data into actionable insights for non-technical stakeholders.

• Storytelling – Data storytelling is a technique for communicating insights from data through narrative and visualization.

• Language Proficiency – Language proficiency refers to the ability of a data scientist to efficiently and easily understand the rules and features of a programming language.

Education and Experience

Most data science engineers have a postgraduate degree, and employers usually require one. Data scientists are in high demand, but the pool of potential candidates is larger than it used to be. However, companies looking to hire data scientists should look beyond a traditional education to find talent. People interested in data science often attend online training, boot camps, and independent learning. Therefore, considering data scientists who have earned certifications in different technologies and tools and have earned skills through self-learning and experience presents a win-win situation.

For many employers, years of experience and training are essential requirements for the job. Companies hiring data scientists often pay them based on the amount of education they have, as well as the experience they have achieved. For example, a data scientist with a bachelor’s degree and six years of experience are equivalent to someone with a Ph.D. with two years of experience.

Screening Candidates

It’s imperative to screen the talent of data scientists willing to join your team. Identifying candidates with the data science skills required to do the job can be challenging. Ensure your technical teams screening candidate profiles must supplement resume screens with skills testing to gauge a candidate’s actual technical prowess.

Phone screens or online interviews are practical ways of identifying candidates with basic qualifications for a job and sizing up whether they’d be a good fit for the team. The interviewers learn about the candidate’s work experience and interests, and both parties get a feel for each other so that if it looks like there could be a good match, they’ll want to proceed with it.

Skill Assessment

Skill assessments are a part of the hiring process, and they help employers verify the technical skills of the shortlisted candidates. The timing of skill assessments will vary by company, but the sooner you can identify qualified candidates, the better.

The major data science skills for which you need to assess the candidates are,

• Fundamental Statistics
• Applied Mathematics
• Machine Learning
• Working with Database
• Data Understanding and Interpretation
• Problem-solving
• Coding in Data Science Dominant Programming Languages

Onboarding

Following the procedure mentioned here, hiring a data scientist who suits your needs becomes easier. Once you have the right candidate, it’s time to onboard the new data scientist and leverages their expertise in offering data science consulting to kick-start your data-centric journey.

Ensure that you assign a mentor to them and introduce them to the organization’s key stakeholders and other team members. Explain to them your expectations and start by giving them small projects at first.

Once new data scientists become acquainted with their roles, get a hold of their responsibilities, and understand the expectations, they will adjust to meet your expectations.

Data Scientist vs Data Engineer

Frequently Asked Questions (FAQs)

Which one is better in data scientist vs data engineer ?

Data scientist vs data engineer plays a crucial part in a data-driven culture. Although data scientists are more recognized and more in demand, data engineers are the pillars supporting data scientists to perform their job better. If data scientists have a more focused approach, data engineers are the ones who organize algorithms prepared by data scientists into a production flow. Besides, data scientists do the job when you want data collected, modeled, and structured by data engineers to make sense.

Do data engineers and data scientists differ in their Job roles?

As David Bianco states, “Data Engineers are the plumbers building a data pipeline, while data scientists are the painters and storytellers, giving meaning to an otherwise static entity.” Both Data Engineer to Data scientist are part of the same team that seeks to transform raw data into actionable business insights.

Data Engineers and Data Scientists have the most important thing in common: their educational background. Both professionals tend to come from Mathematics, Physics, Computer Science, Information Science, or Computer Engineering backgrounds. Both Data Engineers and Data Scientists are skilled programmers who know how to use languages such as Java, Scala, Python, R, C++, JavaScript, and SQL. However, there is a significant difference between data scientist and data engineer as well as their roles and responsibilities.

Can a data engineer become a data scientist and vice versa?

Yes, data engineers and data scientists can switch roles. Their overlapping skills—from knowledge of programming languages to working with data pipelines—allow them to make an easy transition into the other profession.

However, since data engineers focus on the architecture and infrastructure that supports the work of data scientists, and data scientists develop and test hypotheses through data, both professions would have to brush up on additional skills before they could leap.

How do I hire a good data scientist?

Finding a suitable candidate for a data science position can take hundreds of applications. Companies are beginning to recognize the need to rethink their hiring practices regarding data science. An effective recruitment system can be thought of as like a funnel. The process of hiring a data scientist has been explained above in the blog. However, if you need more information about the same you can contact us at Bacancy.

Is it hard to find a skilled data scientist?

A strong demand exists for data scientists. According to the Bureau of Labor Statistics, the number of data scientist jobs is expected to grow 22 percent over the next decade — much faster than the average growth rate for all jobs.

There is a big demand for data scientists, but the supply of qualified candidates is not meeting that need. As per the McKinsey report, the United States is facing a shortage of approximately 140,000 data scientists. It makes it clear that finding a skilled data scientist matching your needs is hard.

Who is in more demand - a data scientist vs data engineer?

Data scientists and engineers are here to stay, but their roles may shift over time. Data scientists will still be in demand for the creative aspects of the job. Data engineers manage databases and set up Data Modeling environments, while data scientists use knowledge of quantitative science to build predictive models.