Data Engineer vs Data Scientist: Which Career to Choose?

avcontentteam 22 Feb, 2024 • 6 min read

In the world of data, two crucial roles play a significant part in unlocking the power of information: Data Scientists and Data Engineers. But what sets these wizards of data apart? Welcome to the ultimate showdown of Data Scientist vs Data Engineer! In this captivating journey, we’ll explore the distinctive paths these tech titans take to transform raw data into valuable insights.

Data Scientists use statistical expertise and machine learning magic to unearth hidden patterns and predict future trends. On the other hand, Data Engineers are the architects, building robust data pipelines and infrastructure to ensure smooth data flow and storage. Together, they form an unstoppable force that fuels the engines of innovation.

What is Data Engineering?

Data engineering refers to the procedure comprising data organization, storage and processing. Data engineering aims to leverage the potential of data in decision-making through varying analysis methods. Skilled and trained data engineers use advanced tools and technologies to carry out the process. 

Types of Data processing

What is Data Science?

Data science is a multidisciplinary field that dives deep into the field. With a more research-oriented perspective, it functions on the algorithms, processes, scientific methods and systems for knowledge and data extraction. It also utilizes advanced tools and techniques. However, the aim here is data analysis through statistics, artificial intelligence and machine learning. 

What is Data Science
Source: Slide Team

Data Engineering vs Data Science – Overview

AspectData EngineeringData Science
Primary FocusBuilding and maintaining data pipelines and infrastructureAnalyzing and interpreting data to extract insights
Role ObjectiveEnsuring data is collected, stored, and processed efficientlyLeveraging data to make data-driven business decisions
Skills RequiredDatabase management, ETL (Extract, Transform, Load)Statistics, Machine Learning, Data Visualization
Tools and TechHadoop, Spark, SQL, NoSQL databasesPython, R, SQL, TensorFlow, Pandas
Data ManipulationEmphasizes efficient data processing and storageFocuses on data analysis, modeling, and visualization
OutputStructured, clean, and accessible dataValuable insights, predictions, and actionable outcomes
Key ResponsibilitiesDesigning data architectures, data integration, data warehousingExploratory data analysis, predictive modeling, data visualization
Industry ApplicationData infrastructure, data pipelines, big data solutionsBusiness intelligence, predictive analytics, data-driven decision-making
CollaborationCollaborates closely with Data Scientists for data accessibility and qualityCollaborates with Data Engineers for data access and pipeline optimization
GoalSets the foundation for effective data analysisApplies analysis to drive data-based decision-making

Role and Responsibilities

Data Engineer Job Role

  • Work on the complex and new problems arising regularly 
  • Develop big data infrastructure for analysis 
  • Design, build, integrate and test data 
  • Manage, maintain and optimize it as per the individual data requirements
  • Build data pipelines 
  • Write complex queries and data mining 
  • Use ETL or Extra Transform Load for the development of large data warehouses

Data Scientist Job Role

  • Perform online experiments and develop hypotheses
  • Apply statistical analysis and machine learning algorithms on data for trend identification and creating forecasts
  • Visualize and communicate your findings to a technical and non-technical audience 
  • Develop compatible models for

Skills Required

Data Engineer Skills

Technical Skills

  • Deeper understanding and usability of programming languages such as Python, SQL,
  • Ability to handle frameworks  like NoSQL, Data streaming, MapReduce, Hadoop, Hive and Pig
  • Cloud computing 
  • Familiarity with data warehouse platforms such as IBM’s Db2 warehouse and Amazon’s Redshift 
  • Working knowledge of Linux along with Microsoft Windows

Soft Skills

  • Logical mind
  • Ability to identify the data requiring processing and analysis 
  • Able to smoothly function with cross-functional teams 

Data Science Skills

Technical Skills

  • Expertise in programming languages like SAS, R, Python and Java 
  • Proficiency in Big Data frameworks like Spark,
  • Knowledge of the basics of advanced technologies, including Machine Learning and deep learning
  • Ethical knowledge comprising security, biases and privacy 

Soft Skills

  • Out-of-the-box thinking
  • Ability to clearly and concisely explain the technical information in layman’s terms 
  • Ability to work independently 
  • Problem-solving 
  • Broad knowledge of advanced and important concepts 

Data Engineer vs Data Scientist Salary

Data Engineer

The salaries for different levels of experience of data engineers are as follows: 

PositionExperience (years)Average Salary per annum (INR)
Data Engineer/Associate data engineer/ Data Engineer II2-45 – 13 lakhs 
Senior data engineer/Mid-level data engineer/ data engineer III4-510 – 24 lakhs 
Lead data engineer/Team lead data engineer5-717 – 30 lakhs 
Principal data engineer/Senior staff data engineer/Section lead data engineer8+23 – 40 lakhs 

Data Scientist

The salaries at different experience levels for the post of a data scientist are tabulated as follows: 

PositionExperience (years)Average Salary per annum (INR)
Data scientist/data scientist II/Associate data scientist2-47 – 18 lakhs 
Senior data scientist/data scientist III4-516 – 30 lakhs 
Lead data scientist5-718 – 32 lakhs
Principal data scientist8+30 – 60 lakhs

Data Engineer and Data Scientist – companies that will hire you


  • Tech Giants: Google, Microsoft, Amazon, Apple, Facebook
  • Consulting Firms: Accenture, Deloitte, McKinsey & Company, PwC, EY
  • Financial Services: Bank of America, JPMorgan Chase, Goldman Sachs, Citigroup, Barclays
  • Healthcare: Johnson & Johnson, Pfizer, Merck, Abbott Laboratories, UnitedHealth Group
  • Retail: Walmart, Amazon, Target, Home Depot, Kroger
  • Telecommunications: AT&T, Verizon, T-Mobile, Sprint, Comcast


  • Tech Giants: Google, Microsoft, Amazon, Apple, Facebook
  • Consulting Firms: Accenture, Deloitte, McKinsey & Company, PwC, EY
  • Financial Services: Bank of America, JPMorgan Chase, Goldman Sachs, Citigroup, Barclays
  • Healthcare: Johnson & Johnson, Pfizer, Merck, Abbott Laboratories, UnitedHealth Group
  • Retail: Walmart, Amazon, Target, Home Depot, Kroger
  • Telecommunications: AT&T, Verizon, T-Mobile, Sprint, Comcast


  • Data Engineering Startups: Databricks, Confluent, DataRobot, Snowflake, Cloudera
  • Data Science Startups: Palantir, Kensho, Dataiku, Datadog, Stitch

These are just a few examples of the many companies that hire Data Engineers and Data Scientists. The specific companies that hire you will depend on your skills, experience, and location.

Similarities Between Data Engineering and Data Science 

Regardless of the difference between data engineer and data scientist, there are some common points when considering data engineer vs machine learning engineer. They are enlisted as follows:

  • Programming: Knowledge of programming languages for building data pipelines and maintaining databases 
  • Data handling: The common skills here involve
  • Collaboration: They have to collaborate concerning data structure, deciding its compatibility with data analysis and pattern identification 
  • Data quality: Ensuring accuracy and consistency in data is an important task that both professionals need to perform 
  • Business understanding: Domain knowledge is essential for efficient functionality and understanding of the exact requirements 


Effective data handling is crucial for any organization, and skilled professionals are essential for both Data Engineering and Data Science roles. These positions are in high demand, offering many opportunities for career growth and success. Interestingly, a common skill set in these fields allows for a smooth transition between the two, depending on one’s interests and aspirations. Whether you become a data engineer or a data scientist, honing your expertise in either domain promises a bright future filled with promising career prospects. Embrace the world of data, and open the doors to endless possibilities in shaping the fate and reputation of companies through data-driven decisions. Your journey into the world of data begins with boundless potential and opportunities!

Analytics vidhya offers a wide range of courses for data professionals to excel in their careers. You can access these data engineering and data science courses here.

Frequently Asked Questions

Q1. Which is better, data engineering or data science?

A. Both fields are important and rely on each other for data handling. The ‘better’ field among the two depends on one’s interests, skills and career goals. 

Q2. Is data engineering harder than data science?

A. The challenges in both fields vary. While data engineers encounter problems in data processing, pipeline and infrastructure development, data scientists have to deal with ML algorithms, statistics and others. 

Q3. What pays more for data science or data engineering?

A. Data scientists are at senior level and hence are paid comparatively more than data engineers. 

Q4. Can a data engineer be a data scientist?

A. Yes, switching fields is easier by acquiring analytical skills, learning machine language and programming languages and working on data science projects. 

Q5.Who earns more data scientist or MBA?

Data scientists generally earn more than those with an MBA. The specialized skills in data science are in high demand, contributing to higher salaries compared to the broader roles associated with an MBA. If you’re looking for solid earning potential, a career in data science is worth considering

avcontentteam 22 Feb 2024

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